id stringlengths 26 26 | developer stringclasses 1
value | level stringclasses 1
value | task_type stringclasses 14
values | domain stringclasses 12
values | prompt_instruct stringlengths 188 376 | response_instruct stringlengths 286 861 | messages listlengths 3 3 | facts listlengths 2 2 | metadata dict |
|---|---|---|---|---|---|---|---|---|---|
withinus_memorygen_0006401 | WithIn Us AI | god_level | memory_write_new_fact | personalization | [GodLevel MemoryGenesis] Runtime memory write.
Domain: personalization
New information from user:
- Incidents must include trace_id, request_id, and customer impact.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "personalization/db_choice", "value": "Primary database is Postgres; caching via Redis.", "tags": ["architecture", "database", "personalization"], "confidence": 0.94, "ttl_days": 365}
```
User-facing:
- Stored memory `personalization/db_choice` with tags ['architecture... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006402 | WithIn Us AI | god_level | memory_write_new_fact | meeting_minutes | [GodLevel MemoryGenesis] Runtime memory write.
Domain: meeting_minutes
New information from user:
- Service SLO: p95 latency under 200ms and error rate under 0.1%.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing w... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "meeting_minutes/proj_codename", "value": "Project codename is 'Moonlight' and targets offline-first sync.", "tags": ["project", "codename", "meeting_minutes"], "confidence": 0.91, "ttl_days": 365}
```
User-facing:
- Stored memory `meeting_minutes/proj_codename` with t... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Incidents must include trace_id, request_id, and customer impact.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006403 | WithIn Us AI | god_level | memory_write_new_fact | architecture_decisions | [GodLevel MemoryGenesis] Runtime memory write.
Domain: architecture_decisions
New information from user:
- Never log access tokens; redact secrets at ingestion.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing what... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "architecture_decisions/testing_rule", "value": "All bug fixes must include a regression test.", "tags": ["quality", "testing", "architecture_decisions"], "confidence": 0.91}
```
User-facing:
- Stored memory `architecture_decisions/testing_rule` with tags ['quality', '... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"Schema evolution is additive by default; breaking changes require version bump."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006404 | WithIn Us AI | god_level | memory_write_new_fact | deployment_playbooks | [GodLevel MemoryGenesis] Runtime memory write.
Domain: deployment_playbooks
New information from user:
- Primary database is Postgres; caching via Redis.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing what was st... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "deployment_playbooks/pref_editor", "value": "User prefers VS Code keybindings.", "tags": ["preference", "editor", "deployment_playbooks"], "confidence": 0.91}
```
User-facing:
- Stored memory `deployment_playbooks/pref_editor` with tags ['preference', 'editor', 'deplo... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Schema evolution is additive by default; breaking changes require version bump.",
"Project codename is 'Moonlight' and targets offline-first sync."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006405 | WithIn Us AI | god_level | memory_write_new_fact | customer_requirements | [GodLevel MemoryGenesis] Runtime memory write.
Domain: customer_requirements
New information from user:
- User prefers VS Code keybindings.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing what was stored. | TOOL_CALL:
```json
{"tool": "memory.write", "key": "customer_requirements/security_rule", "value": "Never log access tokens; redact secrets at ingestion.", "tags": ["security", "logging", "customer_requirements"], "confidence": 0.87}
```
User-facing:
- Stored memory `customer_requirements/security_rule` with tags ['sec... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"All bug fixes must include a regression test.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006406 | WithIn Us AI | god_level | memory_write_new_fact | product_specs | [GodLevel MemoryGenesis] Runtime memory write.
Domain: product_specs
New information from user:
- Service SLO: p95 latency under 200ms and error rate under 0.1%.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing wha... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "product_specs/deploy_strategy", "value": "Deployments use canary: 1%→10%→50%→100% with rollback triggers.", "tags": ["ops", "deploy", "product_specs"], "confidence": 0.83, "ttl_days": 90}
```
User-facing:
- Stored memory `product_specs/deploy_strategy` with tags ['ops... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers Python for prototypes and Rust for performance-critical services.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006407 | WithIn Us AI | god_level | memory_write_new_fact | personalization | [GodLevel MemoryGenesis] Runtime memory write.
Domain: personalization
New information from user:
- User prefers VS Code keybindings.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing what was stored. | TOOL_CALL:
```json
{"tool": "memory.write", "key": "personalization/pref_language", "value": "User prefers Python for prototypes and Rust for performance-critical services.", "tags": ["preference", "language", "personalization"], "confidence": 0.88}
```
User-facing:
- Stored memory `personalization/pref_language` with ... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"Project codename is 'Moonlight' and targets offline-first sync."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006408 | WithIn Us AI | god_level | memory_write_new_fact | troubleshooting_history | [GodLevel MemoryGenesis] Runtime memory write.
Domain: troubleshooting_history
New information from user:
- Project codename is 'Moonlight' and targets offline-first sync.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summa... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "troubleshooting_history/slo_latency", "value": "Service SLO: p95 latency under 200ms and error rate under 0.1%.", "tags": ["slo", "ops", "troubleshooting_history"], "confidence": 0.81, "ttl_days": 365}
```
User-facing:
- Stored memory `troubleshooting_history/slo_late... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Incidents must include trace_id, request_id, and customer impact.",
"Project codename is 'Moonlight' and targets offline-first sync."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006409 | WithIn Us AI | god_level | memory_write_new_fact | meeting_minutes | [GodLevel MemoryGenesis] Runtime memory write.
Domain: meeting_minutes
New information from user:
- Deployments use canary: 1%→10%→50%→100% with rollback triggers.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing w... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "meeting_minutes/proj_codename", "value": "Project codename is 'Moonlight' and targets offline-first sync.", "tags": ["project", "codename", "meeting_minutes"], "confidence": 0.8, "ttl_days": 90}
```
User-facing:
- Stored memory `meeting_minutes/proj_codename` with tag... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Schema evolution is additive by default; breaking changes require version bump.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006410 | WithIn Us AI | god_level | memory_write_new_fact | personalization | [GodLevel MemoryGenesis] Runtime memory write.
Domain: personalization
New information from user:
- Deployments use canary: 1%→10%→50%→100% with rollback triggers.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing w... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "personalization/security_rule", "value": "Never log access tokens; redact secrets at ingestion.", "tags": ["security", "logging", "personalization"], "confidence": 0.93, "ttl_days": 90}
```
User-facing:
- Stored memory `personalization/security_rule` with tags ['secur... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"Never log access tokens; redact secrets at ingestion."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006411 | WithIn Us AI | god_level | memory_write_new_fact | product_specs | [GodLevel MemoryGenesis] Runtime memory write.
Domain: product_specs
New information from user:
- Project codename is 'Moonlight' and targets offline-first sync.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing wha... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "product_specs/incident_tagging", "value": "Incidents must include trace_id, request_id, and customer impact.", "tags": ["ops", "incident", "product_specs"], "confidence": 0.81}
```
User-facing:
- Stored memory `product_specs/incident_tagging` with tags ['ops', 'incide... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"All bug fixes must include a regression test."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006412 | WithIn Us AI | god_level | memory_write_new_fact | meeting_minutes | [GodLevel MemoryGenesis] Runtime memory write.
Domain: meeting_minutes
New information from user:
- User prefers Python for prototypes and Rust for performance-critical services.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging an... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "meeting_minutes/db_choice", "value": "Primary database is Postgres; caching via Redis.", "tags": ["architecture", "database", "meeting_minutes"], "confidence": 0.88, "ttl_days": 90}
```
User-facing:
- Stored memory `meeting_minutes/db_choice` with tags ['architecture'... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Incidents must include trace_id, request_id, and customer impact.",
"All bug fixes must include a regression test."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006413 | WithIn Us AI | god_level | memory_write_new_fact | customer_requirements | [GodLevel MemoryGenesis] Runtime memory write.
Domain: customer_requirements
New information from user:
- User prefers VS Code keybindings.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing what was stored. | TOOL_CALL:
```json
{"tool": "memory.write", "key": "customer_requirements/proj_codename", "value": "Project codename is 'Moonlight' and targets offline-first sync.", "tags": ["project", "codename", "customer_requirements"], "confidence": 0.88, "ttl_days": 90}
```
User-facing:
- Stored memory `customer_requirements/proj... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers VS Code keybindings.",
"All bug fixes must include a regression test."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006414 | WithIn Us AI | god_level | memory_write_new_fact | troubleshooting_history | [GodLevel MemoryGenesis] Runtime memory write.
Domain: troubleshooting_history
New information from user:
- Primary database is Postgres; caching via Redis.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing what was... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "troubleshooting_history/testing_rule", "value": "All bug fixes must include a regression test.", "tags": ["quality", "testing", "troubleshooting_history"], "confidence": 0.92, "ttl_days": 90}
```
User-facing:
- Stored memory `troubleshooting_history/testing_rule` with... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Schema evolution is additive by default; breaking changes require version bump.",
"Service SLO: p95 latency under 200ms and error rate under 0.1%."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006415 | WithIn Us AI | god_level | memory_write_new_fact | glossary_terms | [GodLevel MemoryGenesis] Runtime memory write.
Domain: glossary_terms
New information from user:
- Service SLO: p95 latency under 200ms and error rate under 0.1%.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing wh... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "glossary_terms/security_rule", "value": "Never log access tokens; redact secrets at ingestion.", "tags": ["security", "logging", "glossary_terms"], "confidence": 0.92, "ttl_days": 365}
```
User-facing:
- Stored memory `glossary_terms/security_rule` with tags ['securit... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"Project codename is 'Moonlight' and targets offline-first sync."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006416 | WithIn Us AI | god_level | memory_write_new_fact | architecture_decisions | [GodLevel MemoryGenesis] Runtime memory write.
Domain: architecture_decisions
New information from user:
- Incidents must include trace_id, request_id, and customer impact.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summ... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "architecture_decisions/incident_tagging", "value": "Incidents must include trace_id, request_id, and customer impact.", "tags": ["ops", "incident", "architecture_decisions"], "confidence": 0.8}
```
User-facing:
- Stored memory `architecture_decisions/incident_tagging`... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006417 | WithIn Us AI | god_level | memory_write_new_fact | architecture_decisions | [GodLevel MemoryGenesis] Runtime memory write.
Domain: architecture_decisions
New information from user:
- Schema evolution is additive by default; breaking changes require version bump.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowle... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "architecture_decisions/pref_language", "value": "User prefers Python for prototypes and Rust for performance-critical services.", "tags": ["preference", "language", "architecture_decisions"], "confidence": 0.88, "ttl_days": 90}
```
User-facing:
- Stored memory `archit... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers VS Code keybindings.",
"Never log access tokens; redact secrets at ingestion."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006418 | WithIn Us AI | god_level | memory_recall_use | customer_requirements | [GodLevel MemoryGenesis] Recall + use memory.
Domain: customer_requirements
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: Service SLO: p95 latency u... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "customer_requirements preferences stack slo", "k": 5, "tags": ["customer_requirements"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement t... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"All bug fixes must include a regression test.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006419 | WithIn Us AI | god_level | memory_recall_use | project_notes | [GodLevel MemoryGenesis] Recall + use memory.
Domain: project_notes
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: User prefers Python for prototypes... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "project_notes preferences stack slo", "k": 5, "tags": ["project_notes"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next feature ... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers Python for prototypes and Rust for performance-critical services.",
"All bug fixes must include a regression test."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006420 | WithIn Us AI | god_level | memory_recall_use | meeting_minutes | [GodLevel MemoryGenesis] Recall + use memory.
Domain: meeting_minutes
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: User prefers VS Code keybindings... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "meeting_minutes preferences stack slo", "k": 5, "tags": ["meeting_minutes"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next feat... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Primary database is Postgres; caching via Redis.",
"Never log access tokens; redact secrets at ingestion."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006421 | WithIn Us AI | god_level | memory_recall_use | project_notes | [GodLevel MemoryGenesis] Recall + use memory.
Domain: project_notes
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: Primary database is Postgres; cach... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "project_notes preferences stack slo", "k": 5, "tags": ["project_notes"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next feature ... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Incidents must include trace_id, request_id, and customer impact.",
"Project codename is 'Moonlight' and targets offline-first sync."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006422 | WithIn Us AI | god_level | memory_recall_use | meeting_minutes | [GodLevel MemoryGenesis] Recall + use memory.
Domain: meeting_minutes
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: Schema evolution is additive by ... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "meeting_minutes preferences stack slo", "k": 5, "tags": ["meeting_minutes"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next feat... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Primary database is Postgres; caching via Redis.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006423 | WithIn Us AI | god_level | memory_recall_use | meeting_minutes | [GodLevel MemoryGenesis] Recall + use memory.
Domain: meeting_minutes
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: Deployments use canary: 1%→10%→5... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "meeting_minutes preferences stack slo", "k": 5, "tags": ["meeting_minutes"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next feat... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"All bug fixes must include a regression test.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006424 | WithIn Us AI | god_level | memory_recall_use | meeting_minutes | [GodLevel MemoryGenesis] Recall + use memory.
Domain: meeting_minutes
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: User prefers VS Code keybindings... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "meeting_minutes preferences stack slo", "k": 5, "tags": ["meeting_minutes"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next feat... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Schema evolution is additive by default; breaking changes require version bump.",
"Project codename is 'Moonlight' and targets offline-first sync."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006425 | WithIn Us AI | god_level | memory_recall_use | user_workflows | [GodLevel MemoryGenesis] Recall + use memory.
Domain: user_workflows
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: Schema evolution is additive by d... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "user_workflows preferences stack slo", "k": 5, "tags": ["user_workflows"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next featur... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"All bug fixes must include a regression test.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006426 | WithIn Us AI | god_level | memory_recall_use | glossary_terms | [GodLevel MemoryGenesis] Recall + use memory.
Domain: glossary_terms
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: Never log access tokens; redact s... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "glossary_terms preferences stack slo", "k": 5, "tags": ["glossary_terms"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next featur... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006427 | WithIn Us AI | god_level | memory_recall_use | coding_preferences | [GodLevel MemoryGenesis] Recall + use memory.
Domain: coding_preferences
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: All bug fixes must include a ... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "coding_preferences preferences stack slo", "k": 5, "tags": ["coding_preferences"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the nex... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006428 | WithIn Us AI | god_level | memory_recall_use | bug_regressions | [GodLevel MemoryGenesis] Recall + use memory.
Domain: bug_regressions
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: User prefers Python for prototyp... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "bug_regressions preferences stack slo", "k": 5, "tags": ["bug_regressions"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next feat... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"All bug fixes must include a regression test.",
"Project codename is 'Moonlight' and targets offline-first sync."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006429 | WithIn Us AI | god_level | memory_recall_use | architecture_decisions | [GodLevel MemoryGenesis] Recall + use memory.
Domain: architecture_decisions
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: Never log access tokens; ... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "architecture_decisions preferences stack slo", "k": 5, "tags": ["architecture_decisions"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006430 | WithIn Us AI | god_level | memory_recall_use | bug_regressions | [GodLevel MemoryGenesis] Recall + use memory.
Domain: bug_regressions
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: Never log access tokens; redact ... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "bug_regressions preferences stack slo", "k": 5, "tags": ["bug_regressions"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next feat... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006431 | WithIn Us AI | god_level | memory_recall_use | deployment_playbooks | [GodLevel MemoryGenesis] Recall + use memory.
Domain: deployment_playbooks
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: User prefers VS Code keybin... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "deployment_playbooks preferences stack slo", "k": 5, "tags": ["deployment_playbooks"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers Python for prototypes and Rust for performance-critical services.",
"Schema evolution is additive by default; breaking changes require version bump."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006432 | WithIn Us AI | god_level | memory_update_correction | project_notes | [GodLevel MemoryGenesis] Update memory with correction.
Domain: project_notes
Previously stored: Primary database is Postgres; caching via Redis.
User correction: The project codename changed from Moonlight to Starlight.
Task: Update the relevant memory, keep an audit note, and respond with the new canonical truth. | TOOL_CALL:
```json
{"tool": "memory.update", "key": "project_notes/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an audit not... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006433 | WithIn Us AI | god_level | memory_update_correction | deployment_playbooks | [GodLevel MemoryGenesis] Update memory with correction.
Domain: deployment_playbooks
Previously stored: Service SLO: p95 latency under 200ms and error rate under 0.1%.
User correction: The project codename changed from Moonlight to Starlight.
Task: Update the relevant memory, keep an audit note, and respond with the ne... | TOOL_CALL:
```json
{"tool": "memory.update", "key": "deployment_playbooks/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an au... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006434 | WithIn Us AI | god_level | memory_update_correction | glossary_terms | [GodLevel MemoryGenesis] Update memory with correction.
Domain: glossary_terms
Previously stored: User prefers Python for prototypes and Rust for performance-critical services.
User correction: User prefers JetBrains shortcuts, not VS Code.
Task: Update the relevant memory, keep an audit note, and respond with the new ... | TOOL_CALL:
```json
{"tool": "memory.update", "key": "glossary_terms/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an audit no... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers Python for prototypes and Rust for performance-critical services.",
"Never log access tokens; redact secrets at ingestion."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006435 | WithIn Us AI | god_level | memory_update_correction | troubleshooting_history | [GodLevel MemoryGenesis] Update memory with correction.
Domain: troubleshooting_history
Previously stored: Never log access tokens; redact secrets at ingestion.
User correction: User prefers JetBrains shortcuts, not VS Code.
Task: Update the relevant memory, keep an audit note, and respond with the new canonical truth. | TOOL_CALL:
```json
{"tool": "memory.update", "key": "troubleshooting_history/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006436 | WithIn Us AI | god_level | memory_update_correction | meeting_minutes | [GodLevel MemoryGenesis] Update memory with correction.
Domain: meeting_minutes
Previously stored: User prefers Python for prototypes and Rust for performance-critical services.
User correction: User prefers JetBrains shortcuts, not VS Code.
Task: Update the relevant memory, keep an audit note, and respond with the new... | TOOL_CALL:
```json
{"tool": "memory.update", "key": "meeting_minutes/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an audit n... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Schema evolution is additive by default; breaking changes require version bump.",
"Service SLO: p95 latency under 200ms and error rate under 0.1%."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006437 | WithIn Us AI | god_level | memory_update_correction | product_specs | [GodLevel MemoryGenesis] Update memory with correction.
Domain: product_specs
Previously stored: Deployments use canary: 1%→10%→50%→100% with rollback triggers.
User correction: Actually the SLO is p95 < 300ms, not 200ms.
Task: Update the relevant memory, keep an audit note, and respond with the new canonical truth. | TOOL_CALL:
```json
{"tool": "memory.update", "key": "product_specs/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an audit not... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"Schema evolution is additive by default; breaking changes require version bump."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006438 | WithIn Us AI | god_level | memory_update_correction | troubleshooting_history | [GodLevel MemoryGenesis] Update memory with correction.
Domain: troubleshooting_history
Previously stored: All bug fixes must include a regression test.
User correction: User prefers JetBrains shortcuts, not VS Code.
Task: Update the relevant memory, keep an audit note, and respond with the new canonical truth. | TOOL_CALL:
```json
{"tool": "memory.update", "key": "troubleshooting_history/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Incidents must include trace_id, request_id, and customer impact.",
"Service SLO: p95 latency under 200ms and error rate under 0.1%."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006439 | WithIn Us AI | god_level | memory_update_correction | architecture_decisions | [GodLevel MemoryGenesis] Update memory with correction.
Domain: architecture_decisions
Previously stored: All bug fixes must include a regression test.
User correction: User prefers JetBrains shortcuts, not VS Code.
Task: Update the relevant memory, keep an audit note, and respond with the new canonical truth. | TOOL_CALL:
```json
{"tool": "memory.update", "key": "architecture_decisions/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an ... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006440 | WithIn Us AI | god_level | memory_update_correction | meeting_minutes | [GodLevel MemoryGenesis] Update memory with correction.
Domain: meeting_minutes
Previously stored: Schema evolution is additive by default; breaking changes require version bump.
User correction: The project codename changed from Moonlight to Starlight.
Task: Update the relevant memory, keep an audit note, and respond ... | TOOL_CALL:
```json
{"tool": "memory.update", "key": "meeting_minutes/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an audit n... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"Project codename is 'Moonlight' and targets offline-first sync."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006441 | WithIn Us AI | god_level | memory_update_correction | user_workflows | [GodLevel MemoryGenesis] Update memory with correction.
Domain: user_workflows
Previously stored: Schema evolution is additive by default; breaking changes require version bump.
User correction: Actually the SLO is p95 < 300ms, not 200ms.
Task: Update the relevant memory, keep an audit note, and respond with the new ca... | TOOL_CALL:
```json
{"tool": "memory.update", "key": "user_workflows/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an audit no... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006442 | WithIn Us AI | god_level | memory_merge_dedup | customer_requirements | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: customer_requirements
Memory entries are duplicative or overlapping:
- A: Deployments use canary: 1%→10%→50%→100% with rollback triggers.
- B: User prefers Python for prototypes and Rust for performance-critical services.
Task: Produce a canonical merged memory and ma... | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "customer_requirements/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["customer_requirements", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoi... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"Service SLO: p95 latency under 200ms and error rate under 0.1%."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006443 | WithIn Us AI | god_level | memory_merge_dedup | user_workflows | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: user_workflows
Memory entries are duplicative or overlapping:
- A: Incidents must include trace_id, request_id, and customer impact.
- B: Deployments use canary: 1%→10%→50%→100% with rollback triggers.
Task: Produce a canonical merged memory and mark duplicates as sup... | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "user_workflows/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["user_workflows", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflicting ... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Incidents must include trace_id, request_id, and customer impact.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006444 | WithIn Us AI | god_level | memory_merge_dedup | bug_regressions | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: bug_regressions
Memory entries are duplicative or overlapping:
- A: Service SLO: p95 latency under 200ms and error rate under 0.1%.
- B: User prefers VS Code keybindings.
Task: Produce a canonical merged memory and mark duplicates as superseded. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "bug_regressions/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["bug_regressions", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflictin... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006445 | WithIn Us AI | god_level | memory_merge_dedup | user_workflows | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: user_workflows
Memory entries are duplicative or overlapping:
- A: User prefers VS Code keybindings.
- B: Deployments use canary: 1%→10%→50%→100% with rollback triggers.
Task: Produce a canonical merged memory and mark duplicates as superseded. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "user_workflows/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["user_workflows", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflicting ... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers Python for prototypes and Rust for performance-critical services.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006446 | WithIn Us AI | god_level | memory_merge_dedup | glossary_terms | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: glossary_terms
Memory entries are duplicative or overlapping:
- A: Deployments use canary: 1%→10%→50%→100% with rollback triggers.
- B: All bug fixes must include a regression test.
Task: Produce a canonical merged memory and mark duplicates as superseded. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "glossary_terms/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["glossary_terms", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflicting ... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers Python for prototypes and Rust for performance-critical services.",
"Project codename is 'Moonlight' and targets offline-first sync."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006447 | WithIn Us AI | god_level | memory_merge_dedup | customer_requirements | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: customer_requirements
Memory entries are duplicative or overlapping:
- A: Project codename is 'Moonlight' and targets offline-first sync.
- B: Service SLO: p95 latency under 200ms and error rate under 0.1%.
Task: Produce a canonical merged memory and mark duplicates a... | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "customer_requirements/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["customer_requirements", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoi... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Incidents must include trace_id, request_id, and customer impact.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006448 | WithIn Us AI | god_level | memory_merge_dedup | bug_regressions | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: bug_regressions
Memory entries are duplicative or overlapping:
- A: User prefers Python for prototypes and Rust for performance-critical services.
- B: Never log access tokens; redact secrets at ingestion.
Task: Produce a canonical merged memory and mark duplicates as... | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "bug_regressions/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["bug_regressions", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflictin... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Primary database is Postgres; caching via Redis.",
"Never log access tokens; redact secrets at ingestion."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006449 | WithIn Us AI | god_level | memory_merge_dedup | bug_regressions | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: bug_regressions
Memory entries are duplicative or overlapping:
- A: Never log access tokens; redact secrets at ingestion.
- B: User prefers Python for prototypes and Rust for performance-critical services.
Task: Produce a canonical merged memory and mark duplicates as... | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "bug_regressions/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["bug_regressions", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflictin... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006450 | WithIn Us AI | god_level | memory_compaction_summary | project_notes | [GodLevel MemoryGenesis] Memory compaction.
Domain: project_notes
Task: Summarize the last 30 days of project memories into a compact 'state of the world' record.
Include: key decisions, current stack, SLOs, open risks, and next steps.
Output: a memory.compact plan + resulting summary memory entry. | TOOL_CALLS:
```json
{"tool": "memory.compact", "scope": "project_notes:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "project_notes/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compressed)."... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers Python for prototypes and Rust for performance-critical services.",
"Never log access tokens; redact secrets at ingestion."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006451 | WithIn Us AI | god_level | memory_compaction_summary | user_workflows | [GodLevel MemoryGenesis] Memory compaction.
Domain: user_workflows
Task: Summarize the last 30 days of project memories into a compact 'state of the world' record.
Include: key decisions, current stack, SLOs, open risks, and next steps.
Output: a memory.compact plan + resulting summary memory entry. | TOOL_CALLS:
```json
{"tool": "memory.compact", "scope": "user_workflows:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "user_workflows/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compressed)... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Primary database is Postgres; caching via Redis.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006452 | WithIn Us AI | god_level | memory_compaction_summary | bug_regressions | [GodLevel MemoryGenesis] Memory compaction.
Domain: bug_regressions
Task: Summarize the last 30 days of project memories into a compact 'state of the world' record.
Include: key decisions, current stack, SLOs, open risks, and next steps.
Output: a memory.compact plan + resulting summary memory entry. | TOOL_CALLS:
```json
{"tool": "memory.compact", "scope": "bug_regressions:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "bug_regressions/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compresse... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Schema evolution is additive by default; breaking changes require version bump.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006453 | WithIn Us AI | god_level | memory_compaction_summary | personalization | [GodLevel MemoryGenesis] Memory compaction.
Domain: personalization
Task: Summarize the last 30 days of project memories into a compact 'state of the world' record.
Include: key decisions, current stack, SLOs, open risks, and next steps.
Output: a memory.compact plan + resulting summary memory entry. | TOOL_CALLS:
```json
{"tool": "memory.compact", "scope": "personalization:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "personalization/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compresse... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"All bug fixes must include a regression test.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006454 | WithIn Us AI | god_level | memory_compaction_summary | bug_regressions | [GodLevel MemoryGenesis] Memory compaction.
Domain: bug_regressions
Task: Summarize the last 30 days of project memories into a compact 'state of the world' record.
Include: key decisions, current stack, SLOs, open risks, and next steps.
Output: a memory.compact plan + resulting summary memory entry. | TOOL_CALLS:
```json
{"tool": "memory.compact", "scope": "bug_regressions:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "bug_regressions/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compresse... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Schema evolution is additive by default; breaking changes require version bump.",
"All bug fixes must include a regression test."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006455 | WithIn Us AI | god_level | memory_compaction_summary | meeting_minutes | [GodLevel MemoryGenesis] Memory compaction.
Domain: meeting_minutes
Task: Summarize the last 30 days of project memories into a compact 'state of the world' record.
Include: key decisions, current stack, SLOs, open risks, and next steps.
Output: a memory.compact plan + resulting summary memory entry. | TOOL_CALLS:
```json
{"tool": "memory.compact", "scope": "meeting_minutes:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "meeting_minutes/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compresse... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Incidents must include trace_id, request_id, and customer impact.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006456 | WithIn Us AI | god_level | memory_compaction_summary | customer_requirements | [GodLevel MemoryGenesis] Memory compaction.
Domain: customer_requirements
Task: Summarize the last 30 days of project memories into a compact 'state of the world' record.
Include: key decisions, current stack, SLOs, open risks, and next steps.
Output: a memory.compact plan + resulting summary memory entry. | TOOL_CALLS:
```json
{"tool": "memory.compact", "scope": "customer_requirements:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "customer_requirements/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next step... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Schema evolution is additive by default; breaking changes require version bump.",
"All bug fixes must include a regression test."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006457 | WithIn Us AI | god_level | memory_compaction_summary | customer_requirements | [GodLevel MemoryGenesis] Memory compaction.
Domain: customer_requirements
Task: Summarize the last 30 days of project memories into a compact 'state of the world' record.
Include: key decisions, current stack, SLOs, open risks, and next steps.
Output: a memory.compact plan + resulting summary memory entry. | TOOL_CALLS:
```json
{"tool": "memory.compact", "scope": "customer_requirements:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "customer_requirements/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next step... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"Never log access tokens; redact secrets at ingestion."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006458 | WithIn Us AI | god_level | memory_conflict_resolution | personalization | [GodLevel MemoryGenesis] Conflict resolution.
Domain: personalization
Two memories conflict:
- M1: 'Primary DB is Postgres'
- M2: 'Primary DB is MySQL'
Task: Resolve using provenance + recency + confidence rules, record the decision, and keep a traceable audit. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "personalization/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["personalization", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "personalization/audit/db_choice", "... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"All bug fixes must include a regression test.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006459 | WithIn Us AI | god_level | memory_conflict_resolution | user_workflows | [GodLevel MemoryGenesis] Conflict resolution.
Domain: user_workflows
Two memories conflict:
- M1: 'Primary DB is Postgres'
- M2: 'Primary DB is MySQL'
Task: Resolve using provenance + recency + confidence rules, record the decision, and keep a traceable audit. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "user_workflows/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["user_workflows", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "user_workflows/audit/db_choice", "val... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Primary database is Postgres; caching via Redis.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006460 | WithIn Us AI | god_level | memory_conflict_resolution | project_notes | [GodLevel MemoryGenesis] Conflict resolution.
Domain: project_notes
Two memories conflict:
- M1: 'Primary DB is Postgres'
- M2: 'Primary DB is MySQL'
Task: Resolve using provenance + recency + confidence rules, record the decision, and keep a traceable audit. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "project_notes/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["project_notes", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "project_notes/audit/db_choice", "value"... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"Service SLO: p95 latency under 200ms and error rate under 0.1%."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006461 | WithIn Us AI | god_level | memory_conflict_resolution | product_specs | [GodLevel MemoryGenesis] Conflict resolution.
Domain: product_specs
Two memories conflict:
- M1: 'Primary DB is Postgres'
- M2: 'Primary DB is MySQL'
Task: Resolve using provenance + recency + confidence rules, record the decision, and keep a traceable audit. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "product_specs/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["product_specs", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "product_specs/audit/db_choice", "value"... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"All bug fixes must include a regression test."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006462 | WithIn Us AI | god_level | memory_conflict_resolution | troubleshooting_history | [GodLevel MemoryGenesis] Conflict resolution.
Domain: troubleshooting_history
Two memories conflict:
- M1: 'Primary DB is Postgres'
- M2: 'Primary DB is MySQL'
Task: Resolve using provenance + recency + confidence rules, record the decision, and keep a traceable audit. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "troubleshooting_history/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["troubleshooting_history", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "troubleshooting_his... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006463 | WithIn Us AI | god_level | memory_conflict_resolution | personalization | [GodLevel MemoryGenesis] Conflict resolution.
Domain: personalization
Two memories conflict:
- M1: 'Primary DB is Postgres'
- M2: 'Primary DB is MySQL'
Task: Resolve using provenance + recency + confidence rules, record the decision, and keep a traceable audit. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "personalization/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["personalization", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "personalization/audit/db_choice", "... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006464 | WithIn Us AI | god_level | memory_conflict_resolution | glossary_terms | [GodLevel MemoryGenesis] Conflict resolution.
Domain: glossary_terms
Two memories conflict:
- M1: 'Primary DB is Postgres'
- M2: 'Primary DB is MySQL'
Task: Resolve using provenance + recency + confidence rules, record the decision, and keep a traceable audit. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "glossary_terms/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["glossary_terms", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "glossary_terms/audit/db_choice", "val... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers Python for prototypes and Rust for performance-critical services.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006465 | WithIn Us AI | god_level | memory_ttl_retention | architecture_decisions | [GodLevel MemoryGenesis] TTL and retention.
Domain: architecture_decisions
Task: Define TTL classes (minutes/days/months/forever) and when to apply them.
Include examples and a cleanup job outline. | TTL classes:
- Minutes-hours: ephemeral session details
- Days (7–30): active tasks, short-lived commitments
- Months (90–365): project state summaries, recurring preferences
- Forever: durable principles (non-sensitive) and stable preferences
Cleanup job:
```text
nightly: delete expired; compact older episodic into s... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers VS Code keybindings.",
"Never log access tokens; redact secrets at ingestion."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006466 | WithIn Us AI | god_level | memory_ttl_retention | deployment_playbooks | [GodLevel MemoryGenesis] TTL and retention.
Domain: deployment_playbooks
Task: Define TTL classes (minutes/days/months/forever) and when to apply them.
Include examples and a cleanup job outline. | TTL classes:
- Minutes-hours: ephemeral session details
- Days (7–30): active tasks, short-lived commitments
- Months (90–365): project state summaries, recurring preferences
- Forever: durable principles (non-sensitive) and stable preferences
Cleanup job:
```text
nightly: delete expired; compact older episodic into s... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"All bug fixes must include a regression test."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006467 | WithIn Us AI | god_level | memory_ttl_retention | coding_preferences | [GodLevel MemoryGenesis] TTL and retention.
Domain: coding_preferences
Task: Define TTL classes (minutes/days/months/forever) and when to apply them.
Include examples and a cleanup job outline. | TTL classes:
- Minutes-hours: ephemeral session details
- Days (7–30): active tasks, short-lived commitments
- Months (90–365): project state summaries, recurring preferences
- Forever: durable principles (non-sensitive) and stable preferences
Cleanup job:
```text
nightly: delete expired; compact older episodic into s... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006468 | WithIn Us AI | god_level | memory_ttl_retention | personalization | [GodLevel MemoryGenesis] TTL and retention.
Domain: personalization
Task: Define TTL classes (minutes/days/months/forever) and when to apply them.
Include examples and a cleanup job outline. | TTL classes:
- Minutes-hours: ephemeral session details
- Days (7–30): active tasks, short-lived commitments
- Months (90–365): project state summaries, recurring preferences
- Forever: durable principles (non-sensitive) and stable preferences
Cleanup job:
```text
nightly: delete expired; compact older episodic into s... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006469 | WithIn Us AI | god_level | memory_ttl_retention | coding_preferences | [GodLevel MemoryGenesis] TTL and retention.
Domain: coding_preferences
Task: Define TTL classes (minutes/days/months/forever) and when to apply them.
Include examples and a cleanup job outline. | TTL classes:
- Minutes-hours: ephemeral session details
- Days (7–30): active tasks, short-lived commitments
- Months (90–365): project state summaries, recurring preferences
- Forever: durable principles (non-sensitive) and stable preferences
Cleanup job:
```text
nightly: delete expired; compact older episodic into s... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers VS Code keybindings.",
"Service SLO: p95 latency under 200ms and error rate under 0.1%."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006470 | WithIn Us AI | god_level | memory_ttl_retention | architecture_decisions | [GodLevel MemoryGenesis] TTL and retention.
Domain: architecture_decisions
Task: Define TTL classes (minutes/days/months/forever) and when to apply them.
Include examples and a cleanup job outline. | TTL classes:
- Minutes-hours: ephemeral session details
- Days (7–30): active tasks, short-lived commitments
- Months (90–365): project state summaries, recurring preferences
- Forever: durable principles (non-sensitive) and stable preferences
Cleanup job:
```text
nightly: delete expired; compact older episodic into s... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006471 | WithIn Us AI | god_level | memory_ttl_retention | personalization | [GodLevel MemoryGenesis] TTL and retention.
Domain: personalization
Task: Define TTL classes (minutes/days/months/forever) and when to apply them.
Include examples and a cleanup job outline. | TTL classes:
- Minutes-hours: ephemeral session details
- Days (7–30): active tasks, short-lived commitments
- Months (90–365): project state summaries, recurring preferences
- Forever: durable principles (non-sensitive) and stable preferences
Cleanup job:
```text
nightly: delete expired; compact older episodic into s... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006472 | WithIn Us AI | god_level | memory_privacy_redaction | product_specs | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: product_specs
User message includes sensitive data: "My API key is sk-live-...".
Task: Refuse to store secrets, redact the sensitive token, store only safe derived info, and respond safely. | User-facing:
- I can’t store or repeat secrets like API keys. I’ve redacted it and will not retain it.
TOOL_CALL:
```json
{"tool": "memory.write", "key": "product_specs/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["product_specs", "security", "redaction"]... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006473 | WithIn Us AI | god_level | memory_privacy_redaction | project_notes | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: project_notes
User message includes sensitive data: "My API key is sk-live-...".
Task: Refuse to store secrets, redact the sensitive token, store only safe derived info, and respond safely. | User-facing:
- I can’t store or repeat secrets like API keys. I’ve redacted it and will not retain it.
TOOL_CALL:
```json
{"tool": "memory.write", "key": "project_notes/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["project_notes", "security", "redaction"]... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Primary database is Postgres; caching via Redis.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006474 | WithIn Us AI | god_level | memory_privacy_redaction | product_specs | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: product_specs
User message includes sensitive data: "My API key is sk-live-...".
Task: Refuse to store secrets, redact the sensitive token, store only safe derived info, and respond safely. | User-facing:
- I can’t store or repeat secrets like API keys. I’ve redacted it and will not retain it.
TOOL_CALL:
```json
{"tool": "memory.write", "key": "product_specs/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["product_specs", "security", "redaction"]... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"Service SLO: p95 latency under 200ms and error rate under 0.1%."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006475 | WithIn Us AI | god_level | memory_privacy_redaction | product_specs | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: product_specs
User message includes sensitive data: "My API key is sk-live-...".
Task: Refuse to store secrets, redact the sensitive token, store only safe derived info, and respond safely. | User-facing:
- I can’t store or repeat secrets like API keys. I’ve redacted it and will not retain it.
TOOL_CALL:
```json
{"tool": "memory.write", "key": "product_specs/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["product_specs", "security", "redaction"]... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"All bug fixes must include a regression test."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006476 | WithIn Us AI | god_level | memory_privacy_redaction | product_specs | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: product_specs
User message includes sensitive data: "My API key is sk-live-...".
Task: Refuse to store secrets, redact the sensitive token, store only safe derived info, and respond safely. | User-facing:
- I can’t store or repeat secrets like API keys. I’ve redacted it and will not retain it.
TOOL_CALL:
```json
{"tool": "memory.write", "key": "product_specs/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["product_specs", "security", "redaction"]... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"All bug fixes must include a regression test.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006477 | WithIn Us AI | god_level | memory_privacy_redaction | architecture_decisions | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: architecture_decisions
User message includes sensitive data: "My API key is sk-live-...".
Task: Refuse to store secrets, redact the sensitive token, store only safe derived info, and respond safely. | User-facing:
- I can’t store or repeat secrets like API keys. I’ve redacted it and will not retain it.
TOOL_CALL:
```json
{"tool": "memory.write", "key": "architecture_decisions/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["architecture_decisions", "secur... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"All bug fixes must include a regression test."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006478 | WithIn Us AI | god_level | memory_routing_policy | troubleshooting_history | [GodLevel MemoryGenesis] Memory routing policy.
Domain: troubleshooting_history
Task: Define what should be stored as memory vs kept in transient context.
Provide a policy with examples and a small rule engine pseudocode. | Policy:
- Store durable memory when it is stable and reusable: preferences, long-lived project constraints, decisions, SLOs.
- Keep transient context for ephemeral items: one-off questions, temporary drafts.
- Never store secrets or highly sensitive identifiers.
Rule engine (pseudocode):
```python
def route(item):
i... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Schema evolution is additive by default; breaking changes require version bump.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006479 | WithIn Us AI | god_level | memory_routing_policy | deployment_playbooks | [GodLevel MemoryGenesis] Memory routing policy.
Domain: deployment_playbooks
Task: Define what should be stored as memory vs kept in transient context.
Provide a policy with examples and a small rule engine pseudocode. | Policy:
- Store durable memory when it is stable and reusable: preferences, long-lived project constraints, decisions, SLOs.
- Keep transient context for ephemeral items: one-off questions, temporary drafts.
- Never store secrets or highly sensitive identifiers.
Rule engine (pseudocode):
```python
def route(item):
i... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers Python for prototypes and Rust for performance-critical services.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006480 | WithIn Us AI | god_level | memory_routing_policy | coding_preferences | [GodLevel MemoryGenesis] Memory routing policy.
Domain: coding_preferences
Task: Define what should be stored as memory vs kept in transient context.
Provide a policy with examples and a small rule engine pseudocode. | Policy:
- Store durable memory when it is stable and reusable: preferences, long-lived project constraints, decisions, SLOs.
- Keep transient context for ephemeral items: one-off questions, temporary drafts.
- Never store secrets or highly sensitive identifiers.
Rule engine (pseudocode):
```python
def route(item):
i... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006481 | WithIn Us AI | god_level | memory_routing_policy | coding_preferences | [GodLevel MemoryGenesis] Memory routing policy.
Domain: coding_preferences
Task: Define what should be stored as memory vs kept in transient context.
Provide a policy with examples and a small rule engine pseudocode. | Policy:
- Store durable memory when it is stable and reusable: preferences, long-lived project constraints, decisions, SLOs.
- Keep transient context for ephemeral items: one-off questions, temporary drafts.
- Never store secrets or highly sensitive identifiers.
Rule engine (pseudocode):
```python
def route(item):
i... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006482 | WithIn Us AI | god_level | memory_routing_policy | personalization | [GodLevel MemoryGenesis] Memory routing policy.
Domain: personalization
Task: Define what should be stored as memory vs kept in transient context.
Provide a policy with examples and a small rule engine pseudocode. | Policy:
- Store durable memory when it is stable and reusable: preferences, long-lived project constraints, decisions, SLOs.
- Keep transient context for ephemeral items: one-off questions, temporary drafts.
- Never store secrets or highly sensitive identifiers.
Rule engine (pseudocode):
```python
def route(item):
i... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Schema evolution is additive by default; breaking changes require version bump.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006483 | WithIn Us AI | god_level | memory_schema_design | customer_requirements | [GodLevel MemoryGenesis] Memory schema design.
Domain: customer_requirements
Task: Design a memory record schema supporting episodic + semantic memory, TTL, confidence, provenance, and embeddings.
Provide JSON schema and explain indexing. | Schema (JSON Schema-style):
```json
{"type": "object", "required": ["key", "kind", "value", "tags", "created_at", "confidence"], "properties": {"key": {"type": "string"}, "kind": {"type": "string", "enum": ["episodic", "semantic", "preference", "decision", "constraint", "summary"]}, "value": {"type": "string"}, "tags":... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"All bug fixes must include a regression test.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006484 | WithIn Us AI | god_level | memory_schema_design | personalization | [GodLevel MemoryGenesis] Memory schema design.
Domain: personalization
Task: Design a memory record schema supporting episodic + semantic memory, TTL, confidence, provenance, and embeddings.
Provide JSON schema and explain indexing. | Schema (JSON Schema-style):
```json
{"type": "object", "required": ["key", "kind", "value", "tags", "created_at", "confidence"], "properties": {"key": {"type": "string"}, "kind": {"type": "string", "enum": ["episodic", "semantic", "preference", "decision", "constraint", "summary"]}, "value": {"type": "string"}, "tags":... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers Python for prototypes and Rust for performance-critical services.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006485 | WithIn Us AI | god_level | memory_schema_design | user_workflows | [GodLevel MemoryGenesis] Memory schema design.
Domain: user_workflows
Task: Design a memory record schema supporting episodic + semantic memory, TTL, confidence, provenance, and embeddings.
Provide JSON schema and explain indexing. | Schema (JSON Schema-style):
```json
{"type": "object", "required": ["key", "kind", "value", "tags", "created_at", "confidence"], "properties": {"key": {"type": "string"}, "kind": {"type": "string", "enum": ["episodic", "semantic", "preference", "decision", "constraint", "summary"]}, "value": {"type": "string"}, "tags":... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"Project codename is 'Moonlight' and targets offline-first sync."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006486 | WithIn Us AI | god_level | memory_schema_design | customer_requirements | [GodLevel MemoryGenesis] Memory schema design.
Domain: customer_requirements
Task: Design a memory record schema supporting episodic + semantic memory, TTL, confidence, provenance, and embeddings.
Provide JSON schema and explain indexing. | Schema (JSON Schema-style):
```json
{"type": "object", "required": ["key", "kind", "value", "tags", "created_at", "confidence"], "properties": {"key": {"type": "string"}, "kind": {"type": "string", "enum": ["episodic", "semantic", "preference", "decision", "constraint", "summary"]}, "value": {"type": "string"}, "tags":... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006487 | WithIn Us AI | god_level | memory_index_vector_hybrid | bug_regressions | [GodLevel MemoryGenesis] Hybrid index.
Domain: bug_regressions
Task: Propose a hybrid retrieval design combining keyword (BM25) + vector similarity + recency.
Include ranking formula and failure modes. | Hybrid retrieval:
- Candidate generation: (1) BM25 keyword over tags/value, (2) vector similarity, (3) recent items.
- Rank: score = 0.45*vec + 0.35*bm25 + 0.15*recency + 0.05*confidence.
Failure modes:
- Stale memories outrank fresh → add recency cap + conflict checks.
- Semantic drift → require provenance and user co... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"All bug fixes must include a regression test.",
"Never log access tokens; redact secrets at ingestion."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006488 | WithIn Us AI | god_level | memory_index_vector_hybrid | deployment_playbooks | [GodLevel MemoryGenesis] Hybrid index.
Domain: deployment_playbooks
Task: Propose a hybrid retrieval design combining keyword (BM25) + vector similarity + recency.
Include ranking formula and failure modes. | Hybrid retrieval:
- Candidate generation: (1) BM25 keyword over tags/value, (2) vector similarity, (3) recent items.
- Rank: score = 0.45*vec + 0.35*bm25 + 0.15*recency + 0.05*confidence.
Failure modes:
- Stale memories outrank fresh → add recency cap + conflict checks.
- Semantic drift → require provenance and user co... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006489 | WithIn Us AI | god_level | memory_index_vector_hybrid | user_workflows | [GodLevel MemoryGenesis] Hybrid index.
Domain: user_workflows
Task: Propose a hybrid retrieval design combining keyword (BM25) + vector similarity + recency.
Include ranking formula and failure modes. | Hybrid retrieval:
- Candidate generation: (1) BM25 keyword over tags/value, (2) vector similarity, (3) recent items.
- Rank: score = 0.45*vec + 0.35*bm25 + 0.15*recency + 0.05*confidence.
Failure modes:
- Stale memories outrank fresh → add recency cap + conflict checks.
- Semantic drift → require provenance and user co... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"All bug fixes must include a regression test."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006490 | WithIn Us AI | god_level | memory_index_vector_hybrid | troubleshooting_history | [GodLevel MemoryGenesis] Hybrid index.
Domain: troubleshooting_history
Task: Propose a hybrid retrieval design combining keyword (BM25) + vector similarity + recency.
Include ranking formula and failure modes. | Hybrid retrieval:
- Candidate generation: (1) BM25 keyword over tags/value, (2) vector similarity, (3) recent items.
- Rank: score = 0.45*vec + 0.35*bm25 + 0.15*recency + 0.05*confidence.
Failure modes:
- Stale memories outrank fresh → add recency cap + conflict checks.
- Semantic drift → require provenance and user co... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006491 | WithIn Us AI | god_level | memory_retrieval_prompting | meeting_minutes | [GodLevel MemoryGenesis] Retrieval prompting.
Domain: meeting_minutes
Task: Write a retrieval prompt template that instructs the model to query memory, cite memory keys, and avoid hallucinations.
Include 'when to ask the user' conditions. | Retrieval prompt template:
1) Convert the user request into search queries.
2) Call `memory.search` with tags.
3) Answer using retrieved items; cite keys.
4) If evidence is missing/conflicting, ask the user.
Example instruction:
"Before answering, query memory for relevant keys. Cite memory keys. If conflicting, prese... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"Service SLO: p95 latency under 200ms and error rate under 0.1%."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006492 | WithIn Us AI | god_level | memory_retrieval_prompting | personalization | [GodLevel MemoryGenesis] Retrieval prompting.
Domain: personalization
Task: Write a retrieval prompt template that instructs the model to query memory, cite memory keys, and avoid hallucinations.
Include 'when to ask the user' conditions. | Retrieval prompt template:
1) Convert the user request into search queries.
2) Call `memory.search` with tags.
3) Answer using retrieved items; cite keys.
4) If evidence is missing/conflicting, ask the user.
Example instruction:
"Before answering, query memory for relevant keys. Cite memory keys. If conflicting, prese... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006493 | WithIn Us AI | god_level | memory_retrieval_prompting | troubleshooting_history | [GodLevel MemoryGenesis] Retrieval prompting.
Domain: troubleshooting_history
Task: Write a retrieval prompt template that instructs the model to query memory, cite memory keys, and avoid hallucinations.
Include 'when to ask the user' conditions. | Retrieval prompt template:
1) Convert the user request into search queries.
2) Call `memory.search` with tags.
3) Answer using retrieved items; cite keys.
4) If evidence is missing/conflicting, ask the user.
Example instruction:
"Before answering, query memory for relevant keys. Cite memory keys. If conflicting, prese... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Incidents must include trace_id, request_id, and customer impact.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006494 | WithIn Us AI | god_level | memory_evaluation_harness | glossary_terms | [GodLevel MemoryGenesis] Evaluation harness.
Domain: glossary_terms
Task: Define metrics to evaluate memory quality (precision/recall, staleness, conflict rate).
Provide a small harness plan that runs memory-retrieval tests and scores answers. | Metrics:
- Recall precision@k, conflict rate, staleness rate, user-correction rate.
Harness:
```python
def score(answer, expected_keys):
return int(all(k in answer for k in expected_keys))
```
Run suites: preference recall, constraint adherence, conflict handling, privacy redaction.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006495 | WithIn Us AI | god_level | memory_evaluation_harness | customer_requirements | [GodLevel MemoryGenesis] Evaluation harness.
Domain: customer_requirements
Task: Define metrics to evaluate memory quality (precision/recall, staleness, conflict rate).
Provide a small harness plan that runs memory-retrieval tests and scores answers. | Metrics:
- Recall precision@k, conflict rate, staleness rate, user-correction rate.
Harness:
```python
def score(answer, expected_keys):
return int(all(k in answer for k in expected_keys))
```
Run suites: preference recall, constraint adherence, conflict handling, privacy redaction.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006496 | WithIn Us AI | god_level | memory_evaluation_harness | meeting_minutes | [GodLevel MemoryGenesis] Evaluation harness.
Domain: meeting_minutes
Task: Define metrics to evaluate memory quality (precision/recall, staleness, conflict rate).
Provide a small harness plan that runs memory-retrieval tests and scores answers. | Metrics:
- Recall precision@k, conflict rate, staleness rate, user-correction rate.
Harness:
```python
def score(answer, expected_keys):
return int(all(k in answer for k in expected_keys))
```
Run suites: preference recall, constraint adherence, conflict handling, privacy redaction.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Incidents must include trace_id, request_id, and customer impact.",
"Schema evolution is additive by default; breaking changes require version bump."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006497 | WithIn Us AI | god_level | qna_runtime_knowledge_injection | meeting_minutes | [GodLevel MemoryGenesis] Q&A: Instant knowledge injection without fine-tuning.
Domain: meeting_minutes
Question: How can an LLM appear to 'learn instantly' in production without weight updates?
Answer with a safe, real-world architecture using external memory (RAG), tool calls, and policy gates. | Architecture for instant 'learning' without fine-tuning:
- External memory store (KV + vector index) + retrieval (RAG).
- Write new facts at runtime via `memory.write` after validation.
- Retrieve via `memory.search` and ground answers with cited keys.
- Evolve memory via compaction, dedup, conflict resolution, TTL.
- ... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006498 | WithIn Us AI | god_level | qna_runtime_knowledge_injection | user_workflows | [GodLevel MemoryGenesis] Q&A: Instant knowledge injection without fine-tuning.
Domain: user_workflows
Question: How can an LLM appear to 'learn instantly' in production without weight updates?
Answer with a safe, real-world architecture using external memory (RAG), tool calls, and policy gates. | Architecture for instant 'learning' without fine-tuning:
- External memory store (KV + vector index) + retrieval (RAG).
- Write new facts at runtime via `memory.write` after validation.
- Retrieve via `memory.search` and ground answers with cited keys.
- Evolve memory via compaction, dedup, conflict resolution, TTL.
- ... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers VS Code keybindings.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006499 | WithIn Us AI | god_level | qna_runtime_knowledge_injection | user_workflows | [GodLevel MemoryGenesis] Q&A: Instant knowledge injection without fine-tuning.
Domain: user_workflows
Question: How can an LLM appear to 'learn instantly' in production without weight updates?
Answer with a safe, real-world architecture using external memory (RAG), tool calls, and policy gates. | Architecture for instant 'learning' without fine-tuning:
- External memory store (KV + vector index) + retrieval (RAG).
- Write new facts at runtime via `memory.write` after validation.
- Retrieve via `memory.search` and ground answers with cited keys.
- Evolve memory via compaction, dedup, conflict resolution, TTL.
- ... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers Python for prototypes and Rust for performance-critical services.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0006500 | WithIn Us AI | god_level | memory_write_new_fact | meeting_minutes | [GodLevel MemoryGenesis] Runtime memory write.
Domain: meeting_minutes
New information from user:
- Incidents must include trace_id, request_id, and customer impact.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "meeting_minutes/testing_rule", "value": "All bug fixes must include a regression test.", "tags": ["quality", "testing", "meeting_minutes"], "confidence": 0.84}
```
User-facing:
- Stored memory `meeting_minutes/testing_rule` with tags ['quality', 'testing', 'meeting_mi... | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"All bug fixes must include a regression test.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
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