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_0007701 | WithIn Us AI | god_level | memory_write_new_fact | product_specs | [GodLevel MemoryGenesis] Runtime memory write.
Domain: product_specs
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 w... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "product_specs/security_rule", "value": "Never log access tokens; redact secrets at ingestion.", "tags": ["security", "logging", "product_specs"], "confidence": 0.91, "ttl_days": 90}
```
User-facing:
- Stored memory `product_specs/security_rule` with tags ['security', ... | [
{
"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_0007702 | WithIn Us AI | god_level | memory_write_new_fact | troubleshooting_history | [GodLevel MemoryGenesis] Runtime memory write.
Domain: troubleshooting_history
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 acknowl... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "troubleshooting_history/schema_rule", "value": "Schema evolution is additive by default; breaking changes require version bump.", "tags": ["schema", "compat", "troubleshooting_history"], "confidence": 0.93, "ttl_days": 90}
```
User-facing:
- Stored memory `troubleshoo... | [
{
"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.",
"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_0007703 | WithIn Us AI | god_level | memory_write_new_fact | user_workflows | [GodLevel MemoryGenesis] Runtime memory write.
Domain: user_workflows
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": "user_workflows/security_rule", "value": "Never log access tokens; redact secrets at ingestion.", "tags": ["security", "logging", "user_workflows"], "confidence": 0.9, "ttl_days": 365}
```
User-facing:
- Stored memory `user_workflows/security_rule` with tags ['security... | [
{
"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_0007704 | WithIn Us AI | god_level | memory_write_new_fact | troubleshooting_history | [GodLevel MemoryGenesis] Runtime memory write.
Domain: troubleshooting_history
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 sum... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "troubleshooting_history/pref_editor", "value": "User prefers VS Code keybindings.", "tags": ["preference", "editor", "troubleshooting_history"], "confidence": 0.83}
```
User-facing:
- Stored memory `troubleshooting_history/pref_editor` with tags ['preference', 'editor... | [
{
"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_0007705 | WithIn Us AI | god_level | memory_write_new_fact | troubleshooting_history | [GodLevel MemoryGenesis] Runtime memory write.
Domain: troubleshooting_history
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 summa... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "troubleshooting_history/proj_codename", "value": "Project codename is 'Moonlight' and targets offline-first sync.", "tags": ["project", "codename", "troubleshooting_history"], "confidence": 0.81, "ttl_days": 365}
```
User-facing:
- Stored memory `troubleshooting_histo... | [
{
"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_0007706 | WithIn Us AI | god_level | memory_write_new_fact | coding_preferences | [GodLevel MemoryGenesis] Runtime memory write.
Domain: coding_preferences
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": "coding_preferences/db_choice", "value": "Primary database is Postgres; caching via Redis.", "tags": ["architecture", "database", "coding_preferences"], "confidence": 0.93}
```
User-facing:
- Stored memory `coding_preferences/db_choice` with tags ['architecture', 'data... | [
{
"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_0007707 | WithIn Us AI | god_level | memory_write_new_fact | coding_preferences | [GodLevel MemoryGenesis] Runtime memory write.
Domain: coding_preferences
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 acknowledgin... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "coding_preferences/pref_language", "value": "User prefers Python for prototypes and Rust for performance-critical services.", "tags": ["preference", "language", "coding_preferences"], "confidence": 0.89, "ttl_days": 365}
```
User-facing:
- Stored memory `coding_prefer... | [
{
"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.",
"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_0007708 | WithIn Us AI | god_level | memory_write_new_fact | coding_preferences | [GodLevel MemoryGenesis] Runtime memory write.
Domain: coding_preferences
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 summarizin... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "coding_preferences/pref_editor", "value": "User prefers VS Code keybindings.", "tags": ["preference", "editor", "coding_preferences"], "confidence": 0.83, "ttl_days": 365}
```
User-facing:
- Stored memory `coding_preferences/pref_editor` with tags ['preference', 'edit... | [
{
"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.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0007709 | WithIn Us AI | god_level | memory_write_new_fact | deployment_playbooks | [GodLevel MemoryGenesis] Runtime memory write.
Domain: deployment_playbooks
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 summariz... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "deployment_playbooks/slo_latency", "value": "Service SLO: p95 latency under 200ms and error rate under 0.1%.", "tags": ["slo", "ops", "deployment_playbooks"], "confidence": 0.81}
```
User-facing:
- Stored memory `deployment_playbooks/slo_latency` with tags ['slo', 'op... | [
{
"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.",
"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_0007710 | 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/proj_codename", "value": "Project codename is 'Moonlight' and targets offline-first sync.", "tags": ["project", "codename", "meeting_minutes"], "confidence": 0.81, "ttl_days": 90}
```
User-facing:
- Stored memory `meeting_minutes/proj_codename` with ta... | [
{
"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_0007711 | WithIn Us AI | god_level | memory_write_new_fact | project_notes | [GodLevel MemoryGenesis] Runtime memory write.
Domain: project_notes
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": "project_notes/pref_editor", "value": "User prefers VS Code keybindings.", "tags": ["preference", "editor", "project_notes"], "confidence": 0.85}
```
User-facing:
- Stored memory `project_notes/pref_editor` with tags ['preference', 'editor', 'project_notes']. No TTL (d... | [
{
"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 VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0007712 | WithIn Us AI | god_level | memory_write_new_fact | coding_preferences | [GodLevel MemoryGenesis] Runtime memory write.
Domain: coding_preferences
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": "coding_preferences/slo_latency", "value": "Service SLO: p95 latency under 200ms and error rate under 0.1%.", "tags": ["slo", "ops", "coding_preferences"], "confidence": 0.91, "ttl_days": 365}
```
User-facing:
- Stored memory `coding_preferences/slo_latency` with 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... | [
"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_0007713 | 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/schema_rule", "value": "Schema evolution is additive by default; breaking changes require version bump.", "tags": ["schema", "compat", "product_specs"], "confidence": 0.93, "ttl_days": 365}
```
User-facing:
- Stored memory `product_specs/schema_rule` wit... | [
{
"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_0007714 | WithIn Us AI | god_level | memory_write_new_fact | customer_requirements | [GodLevel MemoryGenesis] Runtime memory write.
Domain: customer_requirements
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 summari... | 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": 0.84, "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... | [
"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_0007715 | 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/slo_latency", "value": "Service SLO: p95 latency under 200ms and error rate under 0.1%.", "tags": ["slo", "ops", "architecture_decisions"], "confidence": 0.91, "ttl_days": 90}
```
User-facing:
- Stored memory `architecture_decisions/slo_latency`... | [
{
"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_0007716 | WithIn Us AI | god_level | memory_write_new_fact | customer_requirements | [GodLevel MemoryGenesis] Runtime memory write.
Domain: customer_requirements
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 summari... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "customer_requirements/db_choice", "value": "Primary database is Postgres; caching via Redis.", "tags": ["architecture", "database", "customer_requirements"], "confidence": 0.91, "ttl_days": 365}
```
User-facing:
- Stored memory `customer_requirements/db_choice` 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... | [
"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_0007717 | WithIn Us AI | god_level | memory_write_new_fact | customer_requirements | [GodLevel MemoryGenesis] Runtime memory write.
Domain: customer_requirements
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 summari... | TOOL_CALL:
```json
{"tool": "memory.write", "key": "customer_requirements/deploy_strategy", "value": "Deployments use canary: 1%→10%→50%→100% with rollback triggers.", "tags": ["ops", "deploy", "customer_requirements"], "confidence": 0.87}
```
User-facing:
- Stored memory `customer_requirements/deploy_strategy` with ta... | [
{
"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.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0007718 | 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: Service SLO: p95 latency unde... | 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... | [
"Incidents must include trace_id, request_id, and customer impact.",
"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_0007719 | WithIn Us AI | god_level | memory_recall_use | product_specs | [GodLevel MemoryGenesis] Recall + use memory.
Domain: product_specs
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 se... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "product_specs preferences stack slo", "k": 5, "tags": ["product_specs"]}
```
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... | [
"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_0007720 | 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 Python for pro... | 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... | [
"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_0007721 | 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: Project codename is 'Moonlight' a... | 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... | [
"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_0007722 | 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: User prefers Python for prototype... | 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... | [
"Primary database is Postgres; caching via Redis.",
"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_0007723 | WithIn Us AI | god_level | memory_recall_use | product_specs | [GodLevel MemoryGenesis] Recall + use memory.
Domain: product_specs
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: Project codename is 'Moonlight' an... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "product_specs preferences stack slo", "k": 5, "tags": ["product_specs"]}
```
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... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"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_0007724 | 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: User prefers Python for pr... | 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... | [
"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_0007725 | 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: Deployments use canary: 1%→10... | 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... | [
"All bug fixes must include a regression test.",
"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_0007726 | 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: User prefers VS Code keybindings.... | 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... | [
"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_0007727 | WithIn Us AI | god_level | memory_recall_use | troubleshooting_history | [GodLevel MemoryGenesis] Recall + use memory.
Domain: troubleshooting_history
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": "troubleshooting_history preferences stack slo", "k": 5, "tags": ["troubleshooting_history"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Impleme... | [
{
"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_0007728 | 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: Project codename is 'Moonl... | 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... | [
"Primary database is Postgres; caching via Redis.",
"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_0007729 | 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: Primary database is Postgres; ca... | 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... | [
"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_0007730 | WithIn Us AI | god_level | memory_recall_use | product_specs | [GodLevel MemoryGenesis] Recall + use memory.
Domain: product_specs
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%→50%... | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "product_specs preferences stack slo", "k": 5, "tags": ["product_specs"]}
```
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... | [
"All bug fixes must include a regression test.",
"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_0007731 | 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: Primary database is Postgres; cac... | 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... | [
"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_0007732 | WithIn Us AI | god_level | memory_update_correction | project_notes | [GodLevel MemoryGenesis] Update memory with correction.
Domain: project_notes
Previously stored: Incidents must include trace_id, request_id, and customer impact.
User correction: The project codename changed from Moonlight to Starlight.
Task: Update the relevant memory, keep an audit note, and respond with the new can... | 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.",
"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_0007733 | 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: User prefers JetBrains shortcuts, not VS Code.
Task: Update the relevant memory, keep an audit note, and respond with the new canonical... | 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... | [
"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_0007734 | WithIn Us AI | god_level | memory_update_correction | troubleshooting_history | [GodLevel MemoryGenesis] Update memory with correction.
Domain: troubleshooting_history
Previously stored: Incidents must include trace_id, request_id, and customer impact.
User correction: The project codename changed from Moonlight to Starlight.
Task: Update the relevant memory, keep an audit note, and respond with t... | 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... | [
"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_0007735 | WithIn Us AI | god_level | memory_update_correction | troubleshooting_history | [GodLevel MemoryGenesis] Update memory with correction.
Domain: troubleshooting_history
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... | 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.",
"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_0007736 | 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... | [
"Primary database is Postgres; caching via Redis.",
"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_0007737 | WithIn Us AI | god_level | memory_update_correction | user_workflows | [GodLevel MemoryGenesis] Update memory with correction.
Domain: user_workflows
Previously stored: Never log access tokens; redact secrets at ingestion.
User correction: We switched from Redis to Memcached for cache.
Task: Update the relevant memory, keep an audit note, and respond with the new canonical truth. | 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... | [
"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_0007738 | WithIn Us AI | god_level | memory_update_correction | coding_preferences | [GodLevel MemoryGenesis] Update memory with correction.
Domain: coding_preferences
Previously stored: Project codename is 'Moonlight' and targets offline-first sync.
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 trut... | TOOL_CALL:
```json
{"tool": "memory.update", "key": "coding_preferences/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 audi... | [
{
"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_0007739 | WithIn Us AI | god_level | memory_update_correction | coding_preferences | [GodLevel MemoryGenesis] Update memory with correction.
Domain: coding_preferences
Previously stored: User prefers Python for prototypes and Rust for performance-critical services.
User correction: Actually the SLO is p95 < 300ms, not 200ms.
Task: Update the relevant memory, keep an audit note, and respond with the new... | TOOL_CALL:
```json
{"tool": "memory.update", "key": "coding_preferences/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 audi... | [
{
"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_0007740 | WithIn Us AI | god_level | memory_update_correction | project_notes | [GodLevel MemoryGenesis] Update memory with correction.
Domain: project_notes
Previously stored: Never log access tokens; redact secrets at ingestion.
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... | [
"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_0007741 | WithIn Us AI | god_level | memory_update_correction | deployment_playbooks | [GodLevel MemoryGenesis] Update memory with correction.
Domain: deployment_playbooks
Previously stored: Incidents must include trace_id, request_id, and customer impact.
User correction: We switched from Redis to Memcached for cache.
Task: Update the relevant memory, keep an audit note, and respond with the new canonic... | 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.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0007742 | 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: User prefers Python for prototypes and Rust for performance-critical services.
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... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"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_0007743 | WithIn Us AI | god_level | memory_merge_dedup | meeting_minutes | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: meeting_minutes
Memory entries are duplicative or overlapping:
- A: Service SLO: p95 latency under 200ms and error rate under 0.1%.
- B: Service SLO: p95 latency under 200ms and error rate under 0.1%.
Task: Produce a canonical merged memory and mark duplicates as supe... | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "meeting_minutes/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["meeting_minutes", "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... | [
"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_0007744 | WithIn Us AI | god_level | memory_merge_dedup | personalization | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: personalization
Memory entries are duplicative or overlapping:
- A: Never log access tokens; redact secrets at ingestion.
- B: Primary database is Postgres; caching via Redis.
Task: Produce a canonical merged memory and mark duplicates as superseded. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "personalization/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["personalization", "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... | [
"Never log access tokens; redact secrets at ingestion.",
"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_0007745 | WithIn Us AI | god_level | memory_merge_dedup | glossary_terms | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: glossary_terms
Memory entries are duplicative or overlapping:
- A: User prefers Python for prototypes and Rust for performance-critical services.
- B: Service SLO: p95 latency under 200ms and error rate under 0.1%.
Task: Produce a canonical merged memory and mark dupl... | 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... | [
"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_0007746 | WithIn Us AI | god_level | memory_merge_dedup | architecture_decisions | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: architecture_decisions
Memory entries are duplicative or overlapping:
- A: All bug fixes must include a regression test.
- B: Never log access tokens; redact secrets at ingestion.
Task: Produce a canonical merged memory and mark duplicates as superseded. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "architecture_decisions/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["architecture_decisions", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to av... | [
{
"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_0007747 | WithIn Us AI | god_level | memory_merge_dedup | architecture_decisions | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: architecture_decisions
Memory entries are duplicative or overlapping:
- A: User prefers Python for prototypes and Rust for performance-critical services.
- B: Incidents must include trace_id, request_id, and customer impact.
Task: Produce a canonical merged memory and... | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "architecture_decisions/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["architecture_decisions", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to av... | [
{
"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%.",
"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_0007748 | WithIn Us AI | god_level | memory_merge_dedup | architecture_decisions | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: architecture_decisions
Memory entries are duplicative or overlapping:
- A: Deployments use canary: 1%→10%→50%→100% with rollback triggers.
- B: Schema evolution is additive by default; breaking changes require version bump.
Task: Produce a canonical merged memory and ... | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "architecture_decisions/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["architecture_decisions", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to av... | [
{
"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.",
"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_0007749 | WithIn Us AI | god_level | memory_merge_dedup | meeting_minutes | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: meeting_minutes
Memory entries are duplicative or overlapping:
- A: Project codename is 'Moonlight' and targets offline-first sync.
- B: User prefers Python for prototypes and Rust for performance-critical services.
Task: Produce a canonical merged memory and mark dup... | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "meeting_minutes/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["meeting_minutes", "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_0007750 | 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.",
"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_0007751 | WithIn Us AI | god_level | memory_compaction_summary | product_specs | [GodLevel MemoryGenesis] Memory compaction.
Domain: product_specs
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": "product_specs:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "product_specs/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_0007752 | 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... | [
"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_0007753 | 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... | [
"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_0007754 | 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... | [
"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_0007755 | 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... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"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_0007756 | 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... | [
"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_0007757 | 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.",
"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_0007758 | 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... | [
"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_0007759 | WithIn Us AI | god_level | memory_conflict_resolution | architecture_decisions | [GodLevel MemoryGenesis] Conflict resolution.
Domain: architecture_decisions
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": "architecture_decisions/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["architecture_decisions", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "architecture_decision... | [
{
"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_0007760 | WithIn Us AI | god_level | memory_conflict_resolution | meeting_minutes | [GodLevel MemoryGenesis] Conflict resolution.
Domain: meeting_minutes
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": "meeting_minutes/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["meeting_minutes", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "meeting_minutes/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... | [
"Primary database is Postgres; caching via Redis.",
"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_0007761 | WithIn Us AI | god_level | memory_conflict_resolution | coding_preferences | [GodLevel MemoryGenesis] Conflict resolution.
Domain: coding_preferences
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": "coding_preferences/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["coding_preferences", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "coding_preferences/audit/db_c... | [
{
"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_0007762 | WithIn Us AI | god_level | memory_conflict_resolution | customer_requirements | [GodLevel MemoryGenesis] Conflict resolution.
Domain: customer_requirements
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": "customer_requirements/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["customer_requirements", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "customer_requirements/a... | [
{
"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.",
"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_0007763 | 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 VS Code keybindings.",
"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_0007764 | 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... | [
"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_0007765 | WithIn Us AI | god_level | memory_ttl_retention | meeting_minutes | [GodLevel MemoryGenesis] TTL and retention.
Domain: meeting_minutes
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.",
"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_0007766 | WithIn Us AI | god_level | memory_ttl_retention | meeting_minutes | [GodLevel MemoryGenesis] TTL and retention.
Domain: meeting_minutes
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... | [
"Incidents must include trace_id, request_id, and customer impact.",
"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_0007767 | 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... | [
"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_0007768 | WithIn Us AI | god_level | memory_ttl_retention | bug_regressions | [GodLevel MemoryGenesis] TTL and retention.
Domain: bug_regressions
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 Python for prototypes and Rust for performance-critical services.",
"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_0007769 | WithIn Us AI | god_level | memory_ttl_retention | glossary_terms | [GodLevel MemoryGenesis] TTL and retention.
Domain: glossary_terms
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.",
"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_0007770 | WithIn Us AI | god_level | memory_ttl_retention | meeting_minutes | [GodLevel MemoryGenesis] TTL and retention.
Domain: meeting_minutes
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... | [
"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_0007771 | 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... | [
"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_0007772 | WithIn Us AI | god_level | memory_privacy_redaction | glossary_terms | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: glossary_terms
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": "glossary_terms/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["glossary_terms", "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 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_0007773 | WithIn Us AI | god_level | memory_privacy_redaction | customer_requirements | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: customer_requirements
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": "customer_requirements/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["customer_requirements", "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... | [
"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_0007774 | 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... | [
"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_0007775 | WithIn Us AI | god_level | memory_privacy_redaction | personalization | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: personalization
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": "personalization/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["personalization", "security", "redacti... | [
{
"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_0007776 | WithIn Us AI | god_level | memory_privacy_redaction | deployment_playbooks | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: deployment_playbooks
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": "deployment_playbooks/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["deployment_playbooks", "security"... | [
{
"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_0007777 | 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... | [
"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_0007778 | WithIn Us AI | god_level | memory_routing_policy | project_notes | [GodLevel MemoryGenesis] Memory routing policy.
Domain: project_notes
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... | [
"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_0007779 | WithIn Us AI | god_level | memory_routing_policy | project_notes | [GodLevel MemoryGenesis] Memory routing policy.
Domain: project_notes
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.",
"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_0007780 | 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.",
"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_0007781 | WithIn Us AI | god_level | memory_routing_policy | product_specs | [GodLevel MemoryGenesis] Memory routing policy.
Domain: product_specs
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... | [
"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_0007782 | WithIn Us AI | god_level | memory_routing_policy | user_workflows | [GodLevel MemoryGenesis] Memory routing policy.
Domain: user_workflows
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... | [
"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_0007783 | 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... | [
"All bug fixes must include a regression test.",
"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_0007784 | WithIn Us AI | god_level | memory_schema_design | product_specs | [GodLevel MemoryGenesis] Memory schema design.
Domain: product_specs
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... | [
"Primary database is Postgres; caching via Redis.",
"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_0007785 | WithIn Us AI | god_level | memory_schema_design | architecture_decisions | [GodLevel MemoryGenesis] Memory schema design.
Domain: architecture_decisions
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%.",
"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_0007786 | WithIn Us AI | god_level | memory_schema_design | architecture_decisions | [GodLevel MemoryGenesis] Memory schema design.
Domain: architecture_decisions
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... | [
"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_0007787 | WithIn Us AI | god_level | memory_index_vector_hybrid | product_specs | [GodLevel MemoryGenesis] Hybrid index.
Domain: product_specs
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... | [
"Schema evolution is additive by default; breaking changes require version bump.",
"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_0007788 | WithIn Us AI | god_level | memory_index_vector_hybrid | project_notes | [GodLevel MemoryGenesis] Hybrid index.
Domain: project_notes
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... | [
"User prefers Python for prototypes and Rust for performance-critical services.",
"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_0007789 | WithIn Us AI | god_level | memory_index_vector_hybrid | coding_preferences | [GodLevel MemoryGenesis] Hybrid index.
Domain: coding_preferences
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... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"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_0007790 | WithIn Us AI | god_level | memory_index_vector_hybrid | glossary_terms | [GodLevel MemoryGenesis] Hybrid index.
Domain: glossary_terms
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.",
"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_0007791 | WithIn Us AI | god_level | memory_retrieval_prompting | user_workflows | [GodLevel MemoryGenesis] Retrieval prompting.
Domain: user_workflows
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... | [
"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_0007792 | WithIn Us AI | god_level | memory_retrieval_prompting | product_specs | [GodLevel MemoryGenesis] Retrieval prompting.
Domain: product_specs
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... | [
"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_0007793 | WithIn Us AI | god_level | memory_retrieval_prompting | user_workflows | [GodLevel MemoryGenesis] Retrieval prompting.
Domain: user_workflows
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.",
"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_0007794 | 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... | [
"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_0007795 | 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... | [
"Incidents must include trace_id, request_id, and customer impact.",
"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_0007796 | WithIn Us AI | god_level | memory_evaluation_harness | coding_preferences | [GodLevel MemoryGenesis] Evaluation harness.
Domain: coding_preferences
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... | [
"All bug fixes must include a regression test.",
"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_0007797 | WithIn Us AI | god_level | qna_runtime_knowledge_injection | customer_requirements | [GodLevel MemoryGenesis] Q&A: Instant knowledge injection without fine-tuning.
Domain: customer_requirements
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.",
"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_0007798 | WithIn Us AI | god_level | qna_runtime_knowledge_injection | product_specs | [GodLevel MemoryGenesis] Q&A: Instant knowledge injection without fine-tuning.
Domain: product_specs
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... | [
"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_0007799 | WithIn Us AI | god_level | qna_runtime_knowledge_injection | glossary_terms | [GodLevel MemoryGenesis] Q&A: Instant knowledge injection without fine-tuning.
Domain: glossary_terms
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... | [
"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_0007800 | WithIn Us AI | god_level | memory_write_new_fact | bug_regressions | [GodLevel MemoryGenesis] Runtime memory write.
Domain: bug_regressions
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": "bug_regressions/proj_codename", "value": "Project codename is 'Moonlight' and targets offline-first sync.", "tags": ["project", "codename", "bug_regressions"], "confidence": 0.81}
```
User-facing:
- Stored memory `bug_regressions/proj_codename` with tags ['project', '... | [
{
"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.",
"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
} |
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