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_0000001 | WithIn Us AI | god_level | memory_write_new_fact | product_specs | [GodLevel MemoryGenesis] Runtime memory write.
Domain: product_specs
New information from user:
- Service SLO: p95 latency under 200ms and error rate under 0.1%.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing what was stored. | TOOL_CALL:
```json
{"tool": "memory.write", "key": "product_specs/testing_rule", "value": "All bug fixes must include a regression test.", "tags": ["quality", "testing", "product_specs"], "confidence": 0.91}
```
User-facing:
- Stored memory `product_specs/testing_rule` with tags ['quality', 'testing', 'product_specs']. No TTL (durable). | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000002 | WithIn Us AI | god_level | memory_write_new_fact | architecture_decisions | [GodLevel MemoryGenesis] Runtime memory write.
Domain: architecture_decisions
New information from user:
- Never log access tokens; redact secrets at ingestion.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing what was stored. | 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": 0.81, "ttl_days": 365}
```
User-facing:
- Stored memory `architecture_decisions/slo_latency` with tags ['slo', 'ops', 'architecture_decisions']. TTL=365d. | [
{
"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_0000003 | 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 summarizing what was stored. | TOOL_CALL:
```json
{"tool": "memory.write", "key": "customer_requirements/slo_latency", "value": "Service SLO: p95 latency under 200ms and error rate under 0.1%.", "tags": ["slo", "ops", "customer_requirements"], "confidence": 0.89, "ttl_days": 365}
```
User-facing:
- Stored memory `customer_requirements/slo_latency` with tags ['slo', 'ops', 'customer_requirements']. TTL=365d. | [
{
"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_0000004 | 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 acknowledging and summarizing what was stored. | TOOL_CALL:
```json
{"tool": "memory.write", "key": "coding_preferences/schema_rule", "value": "Schema evolution is additive by default; breaking changes require version bump.", "tags": ["schema", "compat", "coding_preferences"], "confidence": 0.94, "ttl_days": 365}
```
User-facing:
- Stored memory `coding_preferences/schema_rule` with tags ['schema', 'compat', 'coding_preferences']. TTL=365d. | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000005 | WithIn Us AI | god_level | memory_write_new_fact | deployment_playbooks | [GodLevel MemoryGenesis] Runtime memory write.
Domain: deployment_playbooks
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 and summarizing what was stored. | TOOL_CALL:
```json
{"tool": "memory.write", "key": "deployment_playbooks/proj_codename", "value": "Project codename is 'Moonlight' and targets offline-first sync.", "tags": ["project", "codename", "deployment_playbooks"], "confidence": 0.83, "ttl_days": 365}
```
User-facing:
- Stored memory `deployment_playbooks/proj_codename` with tags ['project', 'codename', 'deployment_playbooks']. TTL=365d. | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers VS Code keybindings.",
"Service SLO: p95 latency under 200ms and error rate under 0.1%."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000006 | WithIn Us AI | god_level | memory_write_new_fact | architecture_decisions | [GodLevel MemoryGenesis] Runtime memory write.
Domain: architecture_decisions
New information from user:
- All bug fixes must include a regression test.
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": "architecture_decisions/testing_rule", "value": "All bug fixes must include a regression test.", "tags": ["quality", "testing", "architecture_decisions"], "confidence": 0.92}
```
User-facing:
- Stored memory `architecture_decisions/testing_rule` with tags ['quality', 'testing', 'architecture_decisions']. No TTL (durable). | [
{
"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_0000007 | 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 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": "meeting_minutes/pref_language", "value": "User prefers Python for prototypes and Rust for performance-critical services.", "tags": ["preference", "language", "meeting_minutes"], "confidence": 0.92, "ttl_days": 365}
```
User-facing:
- Stored memory `meeting_minutes/pref_language` with tags ['preference', 'language', 'meeting_minutes']. TTL=365d. | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000008 | WithIn Us AI | god_level | memory_write_new_fact | troubleshooting_history | [GodLevel MemoryGenesis] Runtime memory write.
Domain: troubleshooting_history
New information from user:
- All bug fixes must include a regression test.
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": "troubleshooting_history/pref_language", "value": "User prefers Python for prototypes and Rust for performance-critical services.", "tags": ["preference", "language", "troubleshooting_history"], "confidence": 0.92, "ttl_days": 90}
```
User-facing:
- Stored memory `troubleshooting_history/pref_language` with tags ['preference', 'language', 'troubleshooting_history']. TTL=90d. | [
{
"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_0000009 | 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/schema_rule", "value": "Schema evolution is additive by default; breaking changes require version bump.", "tags": ["schema", "compat", "coding_preferences"], "confidence": 0.86}
```
User-facing:
- Stored memory `coding_preferences/schema_rule` with tags ['schema', 'compat', 'coding_preferences']. No TTL (durable). | [
{
"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_0000010 | WithIn Us AI | god_level | memory_write_new_fact | user_workflows | [GodLevel MemoryGenesis] Runtime memory write.
Domain: user_workflows
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 what was stored. | TOOL_CALL:
```json
{"tool": "memory.write", "key": "user_workflows/deploy_strategy", "value": "Deployments use canary: 1%→10%→50%→100% with rollback triggers.", "tags": ["ops", "deploy", "user_workflows"], "confidence": 0.8, "ttl_days": 365}
```
User-facing:
- Stored memory `user_workflows/deploy_strategy` with tags ['ops', 'deploy', 'user_workflows']. TTL=365d. | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000011 | 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 what was stored. | TOOL_CALL:
```json
{"tool": "memory.write", "key": "bug_regressions/deploy_strategy", "value": "Deployments use canary: 1%→10%→50%→100% with rollback triggers.", "tags": ["ops", "deploy", "bug_regressions"], "confidence": 0.86, "ttl_days": 365}
```
User-facing:
- Stored memory `bug_regressions/deploy_strategy` with tags ['ops', 'deploy', 'bug_regressions']. TTL=365d. | [
{
"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.",
"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_0000012 | WithIn Us AI | god_level | memory_write_new_fact | troubleshooting_history | [GodLevel MemoryGenesis] Runtime memory write.
Domain: troubleshooting_history
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": "troubleshooting_history/proj_codename", "value": "Project codename is 'Moonlight' and targets offline-first sync.", "tags": ["project", "codename", "troubleshooting_history"], "confidence": 0.83, "ttl_days": 365}
```
User-facing:
- Stored memory `troubleshooting_history/proj_codename` with tags ['project', 'codename', 'troubleshooting_history']. TTL=365d. | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000013 | WithIn Us AI | god_level | memory_write_new_fact | user_workflows | [GodLevel MemoryGenesis] Runtime memory write.
Domain: user_workflows
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 what was stored. | TOOL_CALL:
```json
{"tool": "memory.write", "key": "user_workflows/schema_rule", "value": "Schema evolution is additive by default; breaking changes require version bump.", "tags": ["schema", "compat", "user_workflows"], "confidence": 0.9, "ttl_days": 365}
```
User-facing:
- Stored memory `user_workflows/schema_rule` with tags ['schema', 'compat', 'user_workflows']. TTL=365d. | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers VS Code keybindings.",
"Service SLO: p95 latency under 200ms and error rate under 0.1%."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000014 | WithIn Us AI | god_level | memory_write_new_fact | product_specs | [GodLevel MemoryGenesis] Runtime memory write.
Domain: product_specs
New information from user:
- All bug fixes must include a regression test.
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": "product_specs/pref_editor", "value": "User prefers VS Code keybindings.", "tags": ["preference", "editor", "product_specs"], "confidence": 0.87, "ttl_days": 90}
```
User-facing:
- Stored memory `product_specs/pref_editor` with tags ['preference', 'editor', 'product_specs']. TTL=90d. | [
{
"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_0000015 | WithIn Us AI | god_level | memory_write_new_fact | project_notes | [GodLevel MemoryGenesis] Runtime memory write.
Domain: project_notes
New information from user:
- Deployments use canary: 1%→10%→50%→100% with rollback triggers.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing what was stored. | TOOL_CALL:
```json
{"tool": "memory.write", "key": "project_notes/incident_tagging", "value": "Incidents must include trace_id, request_id, and customer impact.", "tags": ["ops", "incident", "project_notes"], "confidence": 0.94, "ttl_days": 365}
```
User-facing:
- Stored memory `project_notes/incident_tagging` with tags ['ops', 'incident', 'project_notes']. TTL=365d. | [
{
"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_0000016 | WithIn Us AI | god_level | memory_write_new_fact | glossary_terms | [GodLevel MemoryGenesis] Runtime memory write.
Domain: glossary_terms
New information from user:
- All bug fixes must include a regression test.
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": "glossary_terms/proj_codename", "value": "Project codename is 'Moonlight' and targets offline-first sync.", "tags": ["project", "codename", "glossary_terms"], "confidence": 0.91, "ttl_days": 365}
```
User-facing:
- Stored memory `glossary_terms/proj_codename` with tags ['project', 'codename', 'glossary_terms']. TTL=365d. | [
{
"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_0000017 | WithIn Us AI | god_level | memory_write_new_fact | architecture_decisions | [GodLevel MemoryGenesis] Runtime memory write.
Domain: architecture_decisions
New information from user:
- All bug fixes must include a regression test.
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": "architecture_decisions/testing_rule", "value": "All bug fixes must include a regression test.", "tags": ["quality", "testing", "architecture_decisions"], "confidence": 0.81}
```
User-facing:
- Stored memory `architecture_decisions/testing_rule` with tags ['quality', 'testing', 'architecture_decisions']. No TTL (durable). | [
{
"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_0000018 | 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: Project codename is 'Moonlight' and targets offline-first sync.) | 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) Implement the next feature behind a flag; add regression + load tests.
3) Use your preferred language split: Python for iteration, Rust for hot paths.
4) Ship via canary with rollback triggers.
Cite memories by key when available. | [
{
"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_0000019 | 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: Incidents must include trace_id, request_id, and customer impact.) | 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 feature behind a flag; add regression + load tests.
3) Use your preferred language split: Python for iteration, Rust for hot paths.
4) Ship via canary with rollback triggers.
Cite memories by key when available. | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Incidents must include trace_id, request_id, and customer impact.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000020 | WithIn Us AI | god_level | memory_recall_use | customer_requirements | [GodLevel MemoryGenesis] Recall + use memory.
Domain: customer_requirements
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: Service SLO: p95 latency under 200ms and error rate under 0.1%.) | 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 the next feature behind a flag; add regression + load tests.
3) Use your preferred language split: Python for iteration, Rust for hot paths.
4) Ship via canary with rollback triggers.
Cite memories by key when available. | [
{
"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_0000021 | WithIn Us AI | god_level | memory_recall_use | bug_regressions | [GodLevel MemoryGenesis] Recall + use memory.
Domain: bug_regressions
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: All bug fixes must include a regression test.) | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "bug_regressions preferences stack slo", "k": 5, "tags": ["bug_regressions"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next feature behind a flag; add regression + load tests.
3) Use your preferred language split: Python for iteration, Rust for hot paths.
4) Ship via canary with rollback triggers.
Cite memories by key when available. | [
{
"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_0000022 | 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%→50%→100% with rollback triggers.) | 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 next feature behind a flag; add regression + load tests.
3) Use your preferred language split: Python for iteration, Rust for hot paths.
4) Ship via canary with rollback triggers.
Cite memories by key when available. | [
{
"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_0000023 | WithIn Us AI | god_level | memory_recall_use | deployment_playbooks | [GodLevel MemoryGenesis] Recall + use memory.
Domain: deployment_playbooks
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: User prefers VS Code keybindings.) | 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 next feature behind a flag; add regression + load tests.
3) Use your preferred language split: Python for iteration, Rust for hot paths.
4) Ship via canary with rollback triggers.
Cite memories by key when available. | [
{
"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_0000024 | 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: User prefers Python for prototypes and Rust for performance-critical services.) | 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 feature behind a flag; add regression + load tests.
3) Use your preferred language split: Python for iteration, Rust for hot paths.
4) Ship via canary with rollback triggers.
Cite memories by key when available. | [
{
"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_0000025 | WithIn Us AI | god_level | memory_recall_use | project_notes | [GodLevel MemoryGenesis] Recall + use memory.
Domain: project_notes
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: User prefers Python for prototypes and Rust for performance-critical services.) | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "project_notes preferences stack slo", "k": 5, "tags": ["project_notes"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next feature behind a flag; add regression + load tests.
3) Use your preferred language split: Python for iteration, Rust for hot paths.
4) Ship via canary with rollback triggers.
Cite memories by key when available. | [
{
"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_0000026 | 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: Schema evolution is additive by default; breaking changes require version bump.) | 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 next feature behind a flag; add regression + load tests.
3) Use your preferred language split: Python for iteration, Rust for hot paths.
4) Ship via canary with rollback triggers.
Cite memories by key when available. | [
{
"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_0000027 | 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: Project codename is 'Moonlight' and targets offline-first sync.) | 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 next feature behind a flag; add regression + load tests.
3) Use your preferred language split: Python for iteration, Rust for hot paths.
4) Ship via canary with rollback triggers.
Cite memories by key when available. | [
{
"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.",
"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_0000028 | WithIn Us AI | god_level | memory_recall_use | architecture_decisions | [GodLevel MemoryGenesis] Recall + use memory.
Domain: architecture_decisions
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: User prefers Python for prototypes and Rust for performance-critical services.) | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "architecture_decisions preferences stack slo", "k": 5, "tags": ["architecture_decisions"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next feature behind a flag; add regression + load tests.
3) Use your preferred language split: Python for iteration, Rust for hot paths.
4) Ship via canary with rollback triggers.
Cite memories by key when available. | [
{
"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_0000029 | 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: Primary database is Postgres; caching via Redis.) | 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) Implement the next feature behind a flag; add regression + load tests.
3) Use your preferred language split: Python for iteration, Rust for hot paths.
4) Ship via canary with rollback triggers.
Cite memories by key when available. | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000030 | 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 feature behind a flag; add regression + load tests.
3) Use your preferred language split: Python for iteration, Rust for hot paths.
4) Ship via canary with rollback triggers.
Cite memories by key when available. | [
{
"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_0000031 | 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: User prefers Python for prototypes and Rust for performance-critical services.) | 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) Implement the next feature behind a flag; add regression + load tests.
3) Use your preferred language split: Python for iteration, Rust for hot paths.
4) Ship via canary with rollback triggers.
Cite memories by key when available. | [
{
"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_0000032 | WithIn Us AI | god_level | memory_update_correction | deployment_playbooks | [GodLevel MemoryGenesis] Update memory with correction.
Domain: deployment_playbooks
Previously stored: Primary database is Postgres; caching via Redis.
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": "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 audit note. The corrected value is now authoritative. | [
{
"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_0000033 | WithIn Us AI | god_level | memory_update_correction | bug_regressions | [GodLevel MemoryGenesis] Update memory with correction.
Domain: bug_regressions
Previously stored: All bug fixes must include a regression test.
User correction: Actually the SLO is p95 < 300ms, not 200ms.
Task: Update the relevant memory, keep an audit note, and respond with the new canonical truth. | TOOL_CALL:
```json
{"tool": "memory.update", "key": "bug_regressions/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 note. The corrected value is now authoritative. | [
{
"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_0000034 | WithIn Us AI | god_level | memory_update_correction | coding_preferences | [GodLevel MemoryGenesis] Update memory with correction.
Domain: coding_preferences
Previously stored: Primary database is Postgres; caching via Redis.
User correction: User prefers JetBrains shortcuts, not VS Code.
Task: Update the relevant memory, keep an audit note, and respond with the new canonical truth. | TOOL_CALL:
```json
{"tool": "memory.update", "key": "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 audit note. The corrected value is now authoritative. | [
{
"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_0000035 | WithIn Us AI | god_level | memory_update_correction | project_notes | [GodLevel MemoryGenesis] Update memory with correction.
Domain: project_notes
Previously stored: Deployments use canary: 1%→10%→50%→100% with rollback triggers.
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 note. The corrected value is now authoritative. | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"Never log access tokens; redact secrets at ingestion."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000036 | WithIn Us AI | god_level | memory_update_correction | deployment_playbooks | [GodLevel MemoryGenesis] Update memory with correction.
Domain: deployment_playbooks
Previously stored: User prefers Python for prototypes and Rust for performance-critical services.
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": "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 audit note. The corrected value is now authoritative. | [
{
"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_0000037 | WithIn Us AI | god_level | memory_update_correction | project_notes | [GodLevel MemoryGenesis] Update memory with correction.
Domain: project_notes
Previously stored: Primary database is Postgres; caching via Redis.
User correction: 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": "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 note. The corrected value is now authoritative. | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Schema evolution is additive by default; breaking changes require version bump.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000038 | WithIn Us AI | god_level | memory_update_correction | glossary_terms | [GodLevel MemoryGenesis] Update memory with correction.
Domain: glossary_terms
Previously stored: Project codename is 'Moonlight' and targets offline-first sync.
User correction: User prefers JetBrains shortcuts, not VS Code.
Task: Update the relevant memory, keep an audit note, and respond with the new canonical truth. | TOOL_CALL:
```json
{"tool": "memory.update", "key": "glossary_terms/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an audit note. The corrected value is now authoritative. | [
{
"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_0000039 | WithIn Us AI | god_level | memory_update_correction | personalization | [GodLevel MemoryGenesis] Update memory with correction.
Domain: personalization
Previously stored: Primary database is Postgres; caching via Redis.
User correction: The project codename changed from Moonlight to Starlight.
Task: Update the relevant memory, keep an audit note, and respond with the new canonical truth. | TOOL_CALL:
```json
{"tool": "memory.update", "key": "personalization/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 note. The corrected value is now authoritative. | [
{
"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.",
"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_0000040 | WithIn Us AI | god_level | memory_update_correction | bug_regressions | [GodLevel MemoryGenesis] Update memory with correction.
Domain: bug_regressions
Previously stored: Schema evolution is additive by default; breaking changes require version bump.
User correction: User prefers JetBrains shortcuts, not VS Code.
Task: Update the relevant memory, keep an audit note, and respond with the new canonical truth. | TOOL_CALL:
```json
{"tool": "memory.update", "key": "bug_regressions/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 note. The corrected value is now authoritative. | [
{
"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_0000041 | WithIn Us AI | god_level | memory_update_correction | deployment_playbooks | [GodLevel MemoryGenesis] Update memory with correction.
Domain: deployment_playbooks
Previously stored: User prefers Python for prototypes and Rust for performance-critical services.
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": "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 audit note. The corrected value is now authoritative. | [
{
"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_0000042 | WithIn Us AI | god_level | memory_merge_dedup | troubleshooting_history | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: troubleshooting_history
Memory entries are duplicative or overlapping:
- A: Service SLO: p95 latency under 200ms and error rate under 0.1%.
- 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": "troubleshooting_history/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["troubleshooting_history", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflicting recall. | [
{
"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.",
"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_0000043 | WithIn Us AI | god_level | memory_merge_dedup | project_notes | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: project_notes
Memory entries are duplicative or overlapping:
- A: Schema evolution is additive by default; breaking changes require version bump.
- B: Service SLO: p95 latency under 200ms and error rate under 0.1%.
Task: Produce a canonical merged memory and mark duplicates as superseded. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "project_notes/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["project_notes", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflicting recall. | [
{
"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_0000044 | WithIn Us AI | god_level | memory_merge_dedup | deployment_playbooks | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: deployment_playbooks
Memory entries are duplicative or overlapping:
- A: Primary database is Postgres; caching via Redis.
- B: All bug fixes must include a regression test.
Task: Produce a canonical merged memory and mark duplicates as superseded. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "deployment_playbooks/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["deployment_playbooks", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflicting recall. | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"Project codename is 'Moonlight' and targets offline-first sync."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000045 | WithIn Us AI | god_level | memory_merge_dedup | deployment_playbooks | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: deployment_playbooks
Memory entries are duplicative or overlapping:
- A: Schema evolution is additive by default; breaking changes require version bump.
- B: Schema evolution is additive by default; breaking changes require version bump.
Task: Produce a canonical merged memory and mark duplicates as superseded. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "deployment_playbooks/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["deployment_playbooks", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflicting recall. | [
{
"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_0000046 | WithIn Us AI | god_level | memory_merge_dedup | deployment_playbooks | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: deployment_playbooks
Memory entries are duplicative or overlapping:
- A: Never log access tokens; redact secrets at ingestion.
- B: Schema evolution is additive by default; breaking changes require version bump.
Task: Produce a canonical merged memory and mark duplicates as superseded. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "deployment_playbooks/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["deployment_playbooks", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflicting recall. | [
{
"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_0000047 | WithIn Us AI | god_level | memory_merge_dedup | customer_requirements | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: customer_requirements
Memory entries are duplicative or overlapping:
- A: User prefers VS Code keybindings.
- B: Schema evolution is additive by default; breaking changes require version bump.
Task: Produce a canonical merged memory and mark duplicates as superseded. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "customer_requirements/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["customer_requirements", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflicting recall. | [
{
"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_0000048 | 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: Incidents must include trace_id, request_id, and customer impact.
Task: Produce a canonical merged memory and mark duplicates as superseded. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "glossary_terms/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["glossary_terms", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflicting recall. | [
{
"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_0000049 | WithIn Us AI | god_level | memory_merge_dedup | glossary_terms | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: glossary_terms
Memory entries are duplicative or overlapping:
- A: Project codename is 'Moonlight' and targets offline-first sync.
- B: Schema evolution is additive by default; breaking changes require version bump.
Task: Produce a canonical merged memory and mark duplicates as superseded. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "glossary_terms/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["glossary_terms", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflicting recall. | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Primary database is Postgres; caching via Redis.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000050 | WithIn Us AI | god_level | memory_compaction_summary | architecture_decisions | [GodLevel MemoryGenesis] Memory compaction.
Domain: architecture_decisions
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": "architecture_decisions:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "architecture_decisions/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compressed).", "tags": ["architecture_decisions", "summary", "state"], "confidence": 0.84, "ttl_days": 90}
```
User-facing:
- Compacted recent memory into a single summary record and set TTL=90d. | [
{
"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_0000051 | 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).", "tags": ["user_workflows", "summary", "state"], "confidence": 0.84, "ttl_days": 90}
```
User-facing:
- Compacted recent memory into a single summary record and set TTL=90d. | [
{
"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_0000052 | WithIn Us AI | god_level | memory_compaction_summary | personalization | [GodLevel MemoryGenesis] Memory compaction.
Domain: personalization
Task: Summarize the last 30 days of project memories into a compact 'state of the world' record.
Include: key decisions, current stack, SLOs, open risks, and next steps.
Output: a memory.compact plan + resulting summary memory entry. | TOOL_CALLS:
```json
{"tool": "memory.compact", "scope": "personalization:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "personalization/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compressed).", "tags": ["personalization", "summary", "state"], "confidence": 0.84, "ttl_days": 90}
```
User-facing:
- Compacted recent memory into a single summary record and set TTL=90d. | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Schema evolution is additive by default; breaking changes require version bump.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000053 | WithIn Us AI | god_level | memory_compaction_summary | architecture_decisions | [GodLevel MemoryGenesis] Memory compaction.
Domain: architecture_decisions
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": "architecture_decisions:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "architecture_decisions/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compressed).", "tags": ["architecture_decisions", "summary", "state"], "confidence": 0.84, "ttl_days": 90}
```
User-facing:
- Compacted recent memory into a single summary record and set TTL=90d. | [
{
"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_0000054 | WithIn Us AI | god_level | memory_compaction_summary | coding_preferences | [GodLevel MemoryGenesis] Memory compaction.
Domain: coding_preferences
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": "coding_preferences:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "coding_preferences/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compressed).", "tags": ["coding_preferences", "summary", "state"], "confidence": 0.84, "ttl_days": 90}
```
User-facing:
- Compacted recent memory into a single summary record and set TTL=90d. | [
{
"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_0000055 | WithIn Us AI | god_level | memory_compaction_summary | architecture_decisions | [GodLevel MemoryGenesis] Memory compaction.
Domain: architecture_decisions
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": "architecture_decisions:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "architecture_decisions/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compressed).", "tags": ["architecture_decisions", "summary", "state"], "confidence": 0.84, "ttl_days": 90}
```
User-facing:
- Compacted recent memory into a single summary record and set TTL=90d. | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000056 | WithIn Us AI | god_level | memory_compaction_summary | glossary_terms | [GodLevel MemoryGenesis] Memory compaction.
Domain: glossary_terms
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": "glossary_terms:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "glossary_terms/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compressed).", "tags": ["glossary_terms", "summary", "state"], "confidence": 0.84, "ttl_days": 90}
```
User-facing:
- Compacted recent memory into a single summary record and set TTL=90d. | [
{
"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_0000057 | WithIn Us AI | god_level | memory_compaction_summary | personalization | [GodLevel MemoryGenesis] Memory compaction.
Domain: personalization
Task: Summarize the last 30 days of project memories into a compact 'state of the world' record.
Include: key decisions, current stack, SLOs, open risks, and next steps.
Output: a memory.compact plan + resulting summary memory entry. | TOOL_CALLS:
```json
{"tool": "memory.compact", "scope": "personalization:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "personalization/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compressed).", "tags": ["personalization", "summary", "state"], "confidence": 0.84, "ttl_days": 90}
```
User-facing:
- Compacted recent memory into a single summary record and set TTL=90d. | [
{
"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_0000058 | WithIn Us AI | god_level | memory_conflict_resolution | bug_regressions | [GodLevel MemoryGenesis] Conflict resolution.
Domain: bug_regressions
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": "bug_regressions/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["bug_regressions", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "bug_regressions/audit/db_choice", "value": "Resolved conflict M1 vs M2 using recency+provenance; M2 marked superseded.", "tags": ["bug_regressions", "audit", "conflict"], "confidence": 0.8, "ttl_days": 365}
```
User-facing:
- Resolved the conflict using provenance+recency. I will treat Postgres as canonical unless you confirm otherwise. | [
{
"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_0000059 | WithIn Us AI | god_level | memory_conflict_resolution | user_workflows | [GodLevel MemoryGenesis] Conflict resolution.
Domain: user_workflows
Two memories conflict:
- M1: 'Primary DB is Postgres'
- M2: 'Primary DB is MySQL'
Task: Resolve using provenance + recency + confidence rules, record the decision, and keep a traceable audit. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "user_workflows/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["user_workflows", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "user_workflows/audit/db_choice", "value": "Resolved conflict M1 vs M2 using recency+provenance; M2 marked superseded.", "tags": ["user_workflows", "audit", "conflict"], "confidence": 0.8, "ttl_days": 365}
```
User-facing:
- Resolved the conflict using provenance+recency. I will treat Postgres as canonical unless you confirm otherwise. | [
{
"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_0000060 | 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_choice", "value": "Resolved conflict M1 vs M2 using recency+provenance; M2 marked superseded.", "tags": ["coding_preferences", "audit", "conflict"], "confidence": 0.8, "ttl_days": 365}
```
User-facing:
- Resolved the conflict using provenance+recency. I will treat Postgres as canonical unless you confirm otherwise. | [
{
"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_0000061 | WithIn Us AI | god_level | memory_conflict_resolution | bug_regressions | [GodLevel MemoryGenesis] Conflict resolution.
Domain: bug_regressions
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": "bug_regressions/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["bug_regressions", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "bug_regressions/audit/db_choice", "value": "Resolved conflict M1 vs M2 using recency+provenance; M2 marked superseded.", "tags": ["bug_regressions", "audit", "conflict"], "confidence": 0.8, "ttl_days": 365}
```
User-facing:
- Resolved the conflict using provenance+recency. I will treat Postgres as canonical unless you confirm otherwise. | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Never log access tokens; redact secrets at ingestion.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000062 | 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", "value": "Resolved conflict M1 vs M2 using recency+provenance; M2 marked superseded.", "tags": ["glossary_terms", "audit", "conflict"], "confidence": 0.8, "ttl_days": 365}
```
User-facing:
- Resolved the conflict using provenance+recency. I will treat Postgres as canonical unless you confirm otherwise. | [
{
"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_0000063 | WithIn Us AI | god_level | memory_conflict_resolution | deployment_playbooks | [GodLevel MemoryGenesis] Conflict resolution.
Domain: deployment_playbooks
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": "deployment_playbooks/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["deployment_playbooks", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "deployment_playbooks/audit/db_choice", "value": "Resolved conflict M1 vs M2 using recency+provenance; M2 marked superseded.", "tags": ["deployment_playbooks", "audit", "conflict"], "confidence": 0.8, "ttl_days": 365}
```
User-facing:
- Resolved the conflict using provenance+recency. I will treat Postgres as canonical unless you confirm otherwise. | [
{
"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_0000064 | WithIn Us AI | god_level | memory_conflict_resolution | user_workflows | [GodLevel MemoryGenesis] Conflict resolution.
Domain: user_workflows
Two memories conflict:
- M1: 'Primary DB is Postgres'
- M2: 'Primary DB is MySQL'
Task: Resolve using provenance + recency + confidence rules, record the decision, and keep a traceable audit. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "user_workflows/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["user_workflows", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "user_workflows/audit/db_choice", "value": "Resolved conflict M1 vs M2 using recency+provenance; M2 marked superseded.", "tags": ["user_workflows", "audit", "conflict"], "confidence": 0.8, "ttl_days": 365}
```
User-facing:
- Resolved the conflict using provenance+recency. I will treat Postgres as canonical unless you confirm otherwise. | [
{
"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_0000065 | 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 summaries; re-rank by confidence/recency.
```
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Incidents must include trace_id, request_id, and customer impact.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000066 | 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 summaries; re-rank by confidence/recency.
```
| [
{
"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_0000067 | 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 summaries; re-rank by confidence/recency.
```
| [
{
"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_0000068 | 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 summaries; re-rank by confidence/recency.
```
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"All bug fixes must include a regression test.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000069 | 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 summaries; re-rank by confidence/recency.
```
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000070 | WithIn Us AI | god_level | memory_ttl_retention | personalization | [GodLevel MemoryGenesis] TTL and retention.
Domain: personalization
Task: Define TTL classes (minutes/days/months/forever) and when to apply them.
Include examples and a cleanup job outline. | TTL classes:
- Minutes-hours: ephemeral session details
- Days (7–30): active tasks, short-lived commitments
- Months (90–365): project state summaries, recurring preferences
- Forever: durable principles (non-sensitive) and stable preferences
Cleanup job:
```text
nightly: delete expired; compact older episodic into summaries; re-rank by confidence/recency.
```
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Project codename is 'Moonlight' and targets offline-first sync.",
"Never log access tokens; redact secrets at ingestion."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000071 | WithIn Us AI | god_level | memory_ttl_retention | product_specs | [GodLevel MemoryGenesis] TTL and retention.
Domain: product_specs
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 summaries; re-rank by confidence/recency.
```
| [
{
"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_0000072 | 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", "security", "redaction"], "confidence": 0.9}
```
| [
{
"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_0000073 | 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", "redaction"], "confidence": 0.9}
```
| [
{
"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_0000074 | WithIn Us AI | god_level | memory_privacy_redaction | bug_regressions | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: bug_regressions
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": "bug_regressions/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["bug_regressions", "security", "redaction"], "confidence": 0.9}
```
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
End of preview. Expand
in Data Studio
YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)
WithIn Us AI — MemoryGenesis (God Level) 250K
Developer: WithIn Us AI
MemoryGenesis is a 250,000-example dataset designed to train LLMs to behave like memory-first agents:
- capture new information at runtime
- store durable and ephemeral memory safely (TTL)
- retrieve and cite relevant memories (RAG-style behavior)
- update, correct, merge, deduplicate, and compact memories
- resolve conflicts via provenance/recency/confidence
- enforce privacy (no secrets stored), redaction, and safe policies
- approximate "instant knowledge injection" without weight updates by using external memory tools
Important: This dataset trains runtime memory behaviors. It does not perform real-time weight updates or replace model fine-tuning compute; instead it teaches a model to use external memory so new facts can be injected immediately.
Files
train.jsonl— 245,000 examplesvalid.jsonl— 5,000 examplessample_200.jsonl— 200 examples for inspection
Record schema (JSONL)
Each line includes both instruct and chat formats:
prompt_instruct/response_instruct(instruction SFT)messages(chat SFT: system/user/assistant)task_type: memory_write, recall, update, merge/dedup, compaction, TTL, privacy, schema, indexing, eval, Q&Ametadata.runtime_memory_only = trueandmetadata.no_weight_updates = true
Tool protocol (in-text)
Responses may include structured tool calls inside code blocks:
memory.write(key/value/tags/confidence/ttl_days)memory.search(query/k/tags)memory.update/memory.deletememory.compact
These are represented as JSON under TOOL_CALL or TOOL_CALLS.
Safety constraints
- Never store secrets (API keys, passwords, private keys).
- Store only safe derived information.
- Ask user to confirm when evidence is missing or conflicting.
Suggested fine-tuning usage
Instruct SFT
Map:
- input:
prompt_instruct - target:
response_instruct
Chat SFT
Map:
- messages:
messages
Evaluation suggestions
Measure:
- precision@k for retrieval
- staleness/conflict rate
- user correction rate
- privacy redaction compliance
- Downloads last month
- 11