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_0000201 | WithIn Us AI | god_level | memory_write_new_fact | product_specs | [GodLevel MemoryGenesis] Runtime memory write.
Domain: product_specs
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": "product_specs/testing_rule", "value": "All bug fixes must include a regression test.", "tags": ["quality", "testing", "product_specs"], "confidence": 0.95, "ttl_days": 90}
```
User-facing:
- Stored memory `product_specs/testing_rule` with tags ['quality', 'testing', '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... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"Schema evolution is additive by default; breaking changes require version bump."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000202 | WithIn Us AI | god_level | memory_write_new_fact | product_specs | [GodLevel MemoryGenesis] Runtime memory write.
Domain: product_specs
New information from user:
- Incidents must include trace_id, request_id, and customer impact.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing what was stored. | TOOL_CALL:
```json
{"tool": "memory.write", "key": "product_specs/slo_latency", "value": "Service SLO: p95 latency under 200ms and error rate under 0.1%.", "tags": ["slo", "ops", "product_specs"], "confidence": 0.95, "ttl_days": 365}
```
User-facing:
- Stored memory `product_specs/slo_latency` with tags ['slo', 'ops', 'product_specs']. 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... | [
"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_0000203 | WithIn Us AI | god_level | memory_write_new_fact | bug_regressions | [GodLevel MemoryGenesis] Runtime memory write.
Domain: bug_regressions
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": "bug_regressions/proj_codename", "value": "Project codename is 'Moonlight' and targets offline-first sync.", "tags": ["project", "codename", "bug_regressions"], "confidence": 0.88, "ttl_days": 90}
```
User-facing:
- Stored memory `bug_regressions/proj_codename` with tags ['project', 'codename', 'bug_regressions']. 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... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000204 | 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 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": "coding_preferences/proj_codename", "value": "Project codename is 'Moonlight' and targets offline-first sync.", "tags": ["project", "codename", "coding_preferences"], "confidence": 0.89, "ttl_days": 90}
```
User-facing:
- Stored memory `coding_preferences/proj_codename` with tags ['project', 'codename', 'coding_preferences']. 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.",
"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_0000205 | WithIn Us AI | god_level | memory_write_new_fact | glossary_terms | [GodLevel MemoryGenesis] Runtime memory write.
Domain: glossary_terms
New information from user:
- Incidents must include trace_id, request_id, and customer impact.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing what was stored. | TOOL_CALL:
```json
{"tool": "memory.write", "key": "glossary_terms/security_rule", "value": "Never log access tokens; redact secrets at ingestion.", "tags": ["security", "logging", "glossary_terms"], "confidence": 0.81, "ttl_days": 365}
```
User-facing:
- Stored memory `glossary_terms/security_rule` with tags ['security', 'logging', '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... | [
"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_0000206 | 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/pref_language", "value": "User prefers Python for prototypes and Rust for performance-critical services.", "tags": ["preference", "language", "glossary_terms"], "confidence": 0.93, "ttl_days": 365}
```
User-facing:
- Stored memory `glossary_terms/pref_language` with tags ['preference', 'language', '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... | [
"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_0000207 | 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 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": "deployment_playbooks/proj_codename", "value": "Project codename is 'Moonlight' and targets offline-first sync.", "tags": ["project", "codename", "deployment_playbooks"], "confidence": 0.84}
```
User-facing:
- Stored memory `deployment_playbooks/proj_codename` with tags ['project', 'codename', 'deployment_playbooks']. 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... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"Schema evolution is additive by default; breaking changes require version bump."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000208 | WithIn Us AI | god_level | memory_write_new_fact | meeting_minutes | [GodLevel MemoryGenesis] Runtime memory write.
Domain: meeting_minutes
New information from user:
- User prefers Python for prototypes and Rust for performance-critical services.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing what was stored. | TOOL_CALL:
```json
{"tool": "memory.write", "key": "meeting_minutes/db_choice", "value": "Primary database is Postgres; caching via Redis.", "tags": ["architecture", "database", "meeting_minutes"], "confidence": 0.93}
```
User-facing:
- Stored memory `meeting_minutes/db_choice` with tags ['architecture', 'database', 'meeting_minutes']. 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.",
"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_0000209 | WithIn Us AI | god_level | memory_write_new_fact | customer_requirements | [GodLevel MemoryGenesis] Runtime memory write.
Domain: customer_requirements
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": "customer_requirements/security_rule", "value": "Never log access tokens; redact secrets at ingestion.", "tags": ["security", "logging", "customer_requirements"], "confidence": 0.93, "ttl_days": 365}
```
User-facing:
- Stored memory `customer_requirements/security_rule` with tags ['security', 'logging', '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... | [
"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_0000210 | 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": 0.84, "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... | [
"All bug fixes must include a regression test.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000211 | WithIn Us AI | god_level | memory_write_new_fact | bug_regressions | [GodLevel MemoryGenesis] Runtime memory write.
Domain: bug_regressions
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": "bug_regressions/pref_language", "value": "User prefers Python for prototypes and Rust for performance-critical services.", "tags": ["preference", "language", "bug_regressions"], "confidence": 0.88}
```
User-facing:
- Stored memory `bug_regressions/pref_language` with tags ['preference', 'language', 'bug_regressions']. 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.",
"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_0000212 | 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/testing_rule", "value": "All bug fixes must include a regression test.", "tags": ["quality", "testing", "project_notes"], "confidence": 0.95, "ttl_days": 90}
```
User-facing:
- Stored memory `project_notes/testing_rule` with tags ['quality', 'testing', 'project_notes']. 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_0000213 | WithIn Us AI | god_level | memory_write_new_fact | project_notes | [GodLevel MemoryGenesis] Runtime memory write.
Domain: project_notes
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": "project_notes/pref_language", "value": "User prefers Python for prototypes and Rust for performance-critical services.", "tags": ["preference", "language", "project_notes"], "confidence": 0.87, "ttl_days": 365}
```
User-facing:
- Stored memory `project_notes/pref_language` with tags ['preference', 'language', '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... | [
"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_0000214 | WithIn Us AI | god_level | memory_write_new_fact | coding_preferences | [GodLevel MemoryGenesis] Runtime memory write.
Domain: coding_preferences
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": "coding_preferences/schema_rule", "value": "Schema evolution is additive by default; breaking changes require version bump.", "tags": ["schema", "compat", "coding_preferences"], "confidence": 0.85, "ttl_days": 90}
```
User-facing:
- Stored memory `coding_preferences/schema_rule` with tags ['schema', 'compat', 'coding_preferences']. 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.",
"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_0000215 | WithIn Us AI | god_level | memory_write_new_fact | bug_regressions | [GodLevel MemoryGenesis] Runtime memory write.
Domain: bug_regressions
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": "bug_regressions/schema_rule", "value": "Schema evolution is additive by default; breaking changes require version bump.", "tags": ["schema", "compat", "bug_regressions"], "confidence": 0.94}
```
User-facing:
- Stored memory `bug_regressions/schema_rule` with tags ['schema', 'compat', 'bug_regressions']. 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.",
"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_0000216 | WithIn Us AI | god_level | memory_write_new_fact | deployment_playbooks | [GodLevel MemoryGenesis] Runtime memory write.
Domain: deployment_playbooks
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": "deployment_playbooks/pref_language", "value": "User prefers Python for prototypes and Rust for performance-critical services.", "tags": ["preference", "language", "deployment_playbooks"], "confidence": 0.8, "ttl_days": 365}
```
User-facing:
- Stored memory `deployment_playbooks/pref_language` with tags ['preference', 'language', '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... | [
"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_0000217 | 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/slo_latency", "value": "Service SLO: p95 latency under 200ms and error rate under 0.1%.", "tags": ["slo", "ops", "product_specs"], "confidence": 0.95, "ttl_days": 90}
```
User-facing:
- Stored memory `product_specs/slo_latency` with tags ['slo', 'ops', '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.",
"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_0000218 | WithIn Us AI | god_level | memory_recall_use | personalization | [GodLevel MemoryGenesis] Recall + use memory.
Domain: personalization
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": "personalization preferences stack slo", "k": 5, "tags": ["personalization"]}
```
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.",
"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_0000219 | 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... | [
"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_0000220 | WithIn Us AI | god_level | memory_recall_use | product_specs | [GodLevel MemoryGenesis] Recall + use memory.
Domain: product_specs
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: Primary database is Postgres; caching via Redis.) | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "product_specs preferences stack slo", "k": 5, "tags": ["product_specs"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next feature 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.",
"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_0000221 | 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: Incidents must include trace_id, request_id, and customer impact.) | 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... | [
"All bug fixes must include a regression test.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000222 | WithIn Us AI | god_level | memory_recall_use | product_specs | [GodLevel MemoryGenesis] Recall + use memory.
Domain: product_specs
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: Incidents must include trace_id, request_id, and customer impact.) | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "product_specs preferences stack slo", "k": 5, "tags": ["product_specs"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next feature 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.",
"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_0000223 | WithIn Us AI | god_level | memory_recall_use | personalization | [GodLevel MemoryGenesis] Recall + use memory.
Domain: personalization
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": "personalization preferences stack slo", "k": 5, "tags": ["personalization"]}
```
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.",
"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_0000224 | WithIn Us AI | god_level | memory_recall_use | product_specs | [GodLevel MemoryGenesis] Recall + use memory.
Domain: product_specs
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: Primary database is Postgres; caching via Redis.) | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "product_specs preferences stack slo", "k": 5, "tags": ["product_specs"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next feature 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%.",
"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_0000225 | 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: Service SLO: p95 latency under 200ms and error rate under 0.1%.) | 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... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000226 | WithIn Us AI | god_level | memory_recall_use | product_specs | [GodLevel MemoryGenesis] Recall + use memory.
Domain: product_specs
User request: "Use my preferences and current project constraints to recommend next engineering steps."
Task: Search memory for relevant items (e.g., preferences, SLOs, stack) and then answer.
(Assume memory contains: Schema evolution is additive by default; breaking changes require version bump.) | TOOL_CALLS:
```json
{"tool": "memory.search", "query": "product_specs preferences stack slo", "k": 5, "tags": ["product_specs"]}
```
Answer:
- Based on your stored preferences and current constraints, next steps:
1) Confirm SLOs and failure budgets; wire tracing (trace_id/request_id).
2) Implement the next feature 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... | [
"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_0000227 | 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: Project codename is 'Moonlight' and targets offline-first sync.) | 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... | [
"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_0000228 | 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: Never log access tokens; redact secrets at ingestion.) | 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... | [
"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_0000229 | 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... | [
"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_0000230 | 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... | [
"Never log access tokens; redact secrets at ingestion.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000231 | 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 VS Code keybindings.) | 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... | [
"Incidents must include trace_id, request_id, and customer impact.",
"Schema evolution is additive by default; breaking changes require version bump."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000232 | WithIn Us AI | god_level | memory_update_correction | glossary_terms | [GodLevel MemoryGenesis] Update memory with correction.
Domain: glossary_terms
Previously stored: Incidents must include trace_id, request_id, and customer impact.
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": "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... | [
"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_0000233 | 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: 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": "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_0000234 | WithIn Us AI | god_level | memory_update_correction | customer_requirements | [GodLevel MemoryGenesis] Update memory with correction.
Domain: customer_requirements
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": "customer_requirements/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_0000235 | WithIn Us AI | god_level | memory_update_correction | personalization | [GodLevel MemoryGenesis] Update memory with correction.
Domain: personalization
Previously stored: Incidents must include trace_id, request_id, and customer impact.
User correction: We switched from Redis to Memcached for cache.
Task: Update the relevant memory, keep an audit note, and respond with the new 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... | [
"Incidents must include trace_id, request_id, and customer impact.",
"Project codename is 'Moonlight' and targets offline-first sync."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000236 | WithIn Us AI | god_level | memory_update_correction | product_specs | [GodLevel MemoryGenesis] Update memory with correction.
Domain: product_specs
Previously stored: Deployments use canary: 1%→10%→50%→100% with rollback triggers.
User correction: 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": "product_specs/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an audit 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.",
"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_0000237 | WithIn Us AI | god_level | memory_update_correction | troubleshooting_history | [GodLevel MemoryGenesis] Update memory with correction.
Domain: troubleshooting_history
Previously stored: All bug fixes must include a regression test.
User correction: 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": "troubleshooting_history/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an 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.",
"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_0000238 | WithIn Us AI | god_level | memory_update_correction | user_workflows | [GodLevel MemoryGenesis] Update memory with correction.
Domain: user_workflows
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": "user_workflows/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an audit 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... | [
"User prefers Python for prototypes and Rust for performance-critical services.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000239 | WithIn Us AI | god_level | memory_update_correction | deployment_playbooks | [GodLevel MemoryGenesis] Update memory with correction.
Domain: deployment_playbooks
Previously stored: Deployments use canary: 1%→10%→50%→100% with rollback triggers.
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": "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... | [
"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_0000240 | WithIn Us AI | god_level | memory_update_correction | user_workflows | [GodLevel MemoryGenesis] Update memory with correction.
Domain: user_workflows
Previously stored: Incidents must include trace_id, request_id, and customer impact.
User correction: We switched from Redis to Memcached for cache.
Task: Update the relevant memory, keep an audit note, and respond with the new canonical truth. | TOOL_CALL:
```json
{"tool": "memory.update", "key": "user_workflows/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an audit 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... | [
"All bug fixes must include a regression test.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000241 | WithIn Us AI | god_level | memory_update_correction | product_specs | [GodLevel MemoryGenesis] Update memory with correction.
Domain: product_specs
Previously stored: User prefers Python for prototypes and Rust for performance-critical services.
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": "product_specs/canonical", "patch": "Replace old value with corrected value; store 'supersedes' pointer to prior memory id.", "reason": "User provided correction; prefer latest user statement."}
```
User-facing:
- Updated the canonical memory and recorded an audit 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.",
"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_0000242 | WithIn Us AI | god_level | memory_merge_dedup | customer_requirements | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: customer_requirements
Memory entries are duplicative or overlapping:
- A: Deployments use canary: 1%→10%→50%→100% with rollback triggers.
- B: Never log access tokens; redact secrets at ingestion.
Task: Produce a canonical merged memory and mark duplicates as superseded. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "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%.",
"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_0000243 | WithIn Us AI | god_level | memory_merge_dedup | troubleshooting_history | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: troubleshooting_history
Memory entries are duplicative or overlapping:
- A: Schema evolution is additive by default; breaking changes require version bump.
- 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": "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 Python for prototypes and Rust for performance-critical services.",
"Incidents must include trace_id, request_id, and customer impact."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000244 | WithIn Us AI | god_level | memory_merge_dedup | troubleshooting_history | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: troubleshooting_history
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": "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 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_0000245 | 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: Project codename is 'Moonlight' and targets offline-first sync.
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.",
"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_0000246 | WithIn Us AI | god_level | memory_merge_dedup | personalization | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: personalization
Memory entries are duplicative or overlapping:
- A: User prefers Python for prototypes and Rust for performance-critical services.
- B: Deployments use canary: 1%→10%→50%→100% with rollback triggers.
Task: Produce a canonical merged memory and mark duplicates as superseded. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "personalization/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["personalization", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid 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... | [
"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_0000247 | WithIn Us AI | god_level | memory_merge_dedup | bug_regressions | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: bug_regressions
Memory entries are duplicative or overlapping:
- A: User prefers Python for prototypes and Rust for performance-critical services.
- B: 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": "bug_regressions/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["bug_regressions", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid 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.",
"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_0000248 | WithIn Us AI | god_level | memory_merge_dedup | user_workflows | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: user_workflows
Memory entries are duplicative or overlapping:
- A: Project codename is 'Moonlight' and targets offline-first sync.
- B: Service SLO: p95 latency under 200ms and error rate under 0.1%.
Task: Produce a canonical merged memory and mark duplicates as superseded. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "user_workflows/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["user_workflows", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflicting 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... | [
"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_0000249 | WithIn Us AI | god_level | memory_merge_dedup | user_workflows | [GodLevel MemoryGenesis] Merge/deduplicate.
Domain: user_workflows
Memory entries are duplicative or overlapping:
- A: User prefers 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": "user_workflows/merged_profile", "value": "Canonical merged statement capturing both entries without duplication.", "tags": ["user_workflows", "merged", "canonical"], "confidence": 0.86}
```
Notes:
- Mark older entries as `superseded_by` this key to avoid conflicting 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... | [
"Schema evolution is additive by default; breaking changes require version bump.",
"All bug fixes must include a regression test."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000250 | 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... | [
"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_0000251 | WithIn Us AI | god_level | memory_compaction_summary | deployment_playbooks | [GodLevel MemoryGenesis] Memory compaction.
Domain: deployment_playbooks
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": "deployment_playbooks:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "deployment_playbooks/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compressed).", "tags": ["deployment_playbooks", "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.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000252 | 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... | [
"User prefers VS Code keybindings.",
"All bug fixes must include a regression test."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000253 | WithIn Us AI | god_level | memory_compaction_summary | deployment_playbooks | [GodLevel MemoryGenesis] Memory compaction.
Domain: deployment_playbooks
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": "deployment_playbooks:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "deployment_playbooks/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compressed).", "tags": ["deployment_playbooks", "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... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"Service SLO: p95 latency under 200ms and error rate under 0.1%."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000254 | WithIn Us AI | god_level | memory_compaction_summary | project_notes | [GodLevel MemoryGenesis] Memory compaction.
Domain: project_notes
Task: Summarize the last 30 days of project memories into a compact 'state of the world' record.
Include: key decisions, current stack, SLOs, open risks, and next steps.
Output: a memory.compact plan + resulting summary memory entry. | TOOL_CALLS:
```json
{"tool": "memory.compact", "scope": "project_notes:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "project_notes/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compressed).", "tags": ["project_notes", "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%.",
"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_0000255 | WithIn Us AI | god_level | memory_compaction_summary | project_notes | [GodLevel MemoryGenesis] Memory compaction.
Domain: project_notes
Task: Summarize the last 30 days of project memories into a compact 'state of the world' record.
Include: key decisions, current stack, SLOs, open risks, and next steps.
Output: a memory.compact plan + resulting summary memory entry. | TOOL_CALLS:
```json
{"tool": "memory.compact", "scope": "project_notes:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "project_notes/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compressed).", "tags": ["project_notes", "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.",
"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_0000256 | WithIn Us AI | god_level | memory_compaction_summary | meeting_minutes | [GodLevel MemoryGenesis] Memory compaction.
Domain: meeting_minutes
Task: Summarize the last 30 days of project memories into a compact 'state of the world' record.
Include: key decisions, current stack, SLOs, open risks, and next steps.
Output: a memory.compact plan + resulting summary memory entry. | TOOL_CALLS:
```json
{"tool": "memory.compact", "scope": "meeting_minutes:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "meeting_minutes/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compressed).", "tags": ["meeting_minutes", "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.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000257 | WithIn Us AI | god_level | memory_compaction_summary | project_notes | [GodLevel MemoryGenesis] Memory compaction.
Domain: project_notes
Task: Summarize the last 30 days of project memories into a compact 'state of the world' record.
Include: key decisions, current stack, SLOs, open risks, and next steps.
Output: a memory.compact plan + resulting summary memory entry. | TOOL_CALLS:
```json
{"tool": "memory.compact", "scope": "project_notes:last_30_days", "strategy": "summarize+dedup+promote_semantic"}
```
RESULT_MEMORY:
```json
{"tool": "memory.write", "key": "project_notes/state_of_world", "value": "State-of-world summary: decisions, stack, SLOs, open risks, next steps (compressed).", "tags": ["project_notes", "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... | [
"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_0000258 | WithIn Us AI | god_level | memory_conflict_resolution | project_notes | [GodLevel MemoryGenesis] Conflict resolution.
Domain: project_notes
Two memories conflict:
- M1: 'Primary DB is Postgres'
- M2: 'Primary DB is MySQL'
Task: Resolve using provenance + recency + confidence rules, record the decision, and keep a traceable audit. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "project_notes/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["project_notes", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "project_notes/audit/db_choice", "value": "Resolved conflict M1 vs M2 using recency+provenance; M2 marked superseded.", "tags": ["project_notes", "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 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_0000259 | WithIn Us AI | god_level | memory_conflict_resolution | personalization | [GodLevel MemoryGenesis] Conflict resolution.
Domain: personalization
Two memories conflict:
- M1: 'Primary DB is Postgres'
- M2: 'Primary DB is MySQL'
Task: Resolve using provenance + recency + confidence rules, record the decision, and keep a traceable audit. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "personalization/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["personalization", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "personalization/audit/db_choice", "value": "Resolved conflict M1 vs M2 using recency+provenance; M2 marked superseded.", "tags": ["personalization", "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.",
"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_0000260 | 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... | [
"User prefers VS Code keybindings.",
"User prefers Python for prototypes and Rust for performance-critical services."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000261 | WithIn Us AI | god_level | memory_conflict_resolution | personalization | [GodLevel MemoryGenesis] Conflict resolution.
Domain: personalization
Two memories conflict:
- M1: 'Primary DB is Postgres'
- M2: 'Primary DB is MySQL'
Task: Resolve using provenance + recency + confidence rules, record the decision, and keep a traceable audit. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "personalization/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["personalization", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "personalization/audit/db_choice", "value": "Resolved conflict M1 vs M2 using recency+provenance; M2 marked superseded.", "tags": ["personalization", "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.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000262 | WithIn Us AI | god_level | memory_conflict_resolution | troubleshooting_history | [GodLevel MemoryGenesis] Conflict resolution.
Domain: troubleshooting_history
Two memories conflict:
- M1: 'Primary DB is Postgres'
- M2: 'Primary DB is MySQL'
Task: Resolve using provenance + recency + confidence rules, record the decision, and keep a traceable audit. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "troubleshooting_history/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["troubleshooting_history", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "troubleshooting_history/audit/db_choice", "value": "Resolved conflict M1 vs M2 using recency+provenance; M2 marked superseded.", "tags": ["troubleshooting_history", "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.",
"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_0000263 | WithIn Us AI | god_level | memory_conflict_resolution | product_specs | [GodLevel MemoryGenesis] Conflict resolution.
Domain: product_specs
Two memories conflict:
- M1: 'Primary DB is Postgres'
- M2: 'Primary DB is MySQL'
Task: Resolve using provenance + recency + confidence rules, record the decision, and keep a traceable audit. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "product_specs/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["product_specs", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "product_specs/audit/db_choice", "value": "Resolved conflict M1 vs M2 using recency+provenance; M2 marked superseded.", "tags": ["product_specs", "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... | [
"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_0000264 | WithIn Us AI | god_level | memory_conflict_resolution | personalization | [GodLevel MemoryGenesis] Conflict resolution.
Domain: personalization
Two memories conflict:
- M1: 'Primary DB is Postgres'
- M2: 'Primary DB is MySQL'
Task: Resolve using provenance + recency + confidence rules, record the decision, and keep a traceable audit. | TOOL_CALLS:
```json
{"tool": "memory.write", "key": "personalization/db_choice", "value": "Primary DB is Postgres (chosen via most recent + highest confidence).", "tags": ["personalization", "architecture", "database"], "confidence": 0.82}
```
```json
{"tool": "memory.write", "key": "personalization/audit/db_choice", "value": "Resolved conflict M1 vs M2 using recency+provenance; M2 marked superseded.", "tags": ["personalization", "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.",
"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_0000265 | 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... | [
"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_0000266 | WithIn Us AI | god_level | memory_ttl_retention | project_notes | [GodLevel MemoryGenesis] TTL and retention.
Domain: project_notes
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... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000267 | WithIn Us AI | god_level | memory_ttl_retention | customer_requirements | [GodLevel MemoryGenesis] TTL and retention.
Domain: customer_requirements
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 VS Code keybindings.",
"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_0000268 | WithIn Us AI | god_level | memory_ttl_retention | architecture_decisions | [GodLevel MemoryGenesis] TTL and retention.
Domain: architecture_decisions
Task: Define TTL classes (minutes/days/months/forever) and when to apply them.
Include examples and a cleanup job outline. | TTL classes:
- Minutes-hours: ephemeral session details
- Days (7–30): active tasks, short-lived commitments
- Months (90–365): project state summaries, recurring preferences
- Forever: durable principles (non-sensitive) and stable preferences
Cleanup job:
```text
nightly: delete expired; compact older episodic into 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.",
"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_0000269 | WithIn Us AI | god_level | memory_ttl_retention | deployment_playbooks | [GodLevel MemoryGenesis] TTL and retention.
Domain: deployment_playbooks
Task: Define TTL classes (minutes/days/months/forever) and when to apply them.
Include examples and a cleanup job outline. | TTL classes:
- Minutes-hours: ephemeral session details
- Days (7–30): active tasks, short-lived commitments
- Months (90–365): project state summaries, recurring preferences
- Forever: durable principles (non-sensitive) and stable preferences
Cleanup job:
```text
nightly: delete expired; compact older episodic into 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... | [
"Deployments use canary: 1%→10%→50%→100% with rollback triggers.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000270 | WithIn Us AI | god_level | memory_ttl_retention | customer_requirements | [GodLevel MemoryGenesis] TTL and retention.
Domain: customer_requirements
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.",
"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_0000271 | WithIn Us AI | god_level | memory_ttl_retention | architecture_decisions | [GodLevel MemoryGenesis] TTL and retention.
Domain: architecture_decisions
Task: Define TTL classes (minutes/days/months/forever) and when to apply them.
Include examples and a cleanup job outline. | TTL classes:
- Minutes-hours: ephemeral session details
- Days (7–30): active tasks, short-lived commitments
- Months (90–365): project state summaries, recurring preferences
- Forever: durable principles (non-sensitive) and stable preferences
Cleanup job:
```text
nightly: delete expired; compact older episodic into 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.",
"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_0000272 | 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... | [
"All bug fixes must include a regression test.",
"Primary database is Postgres; caching via Redis."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000273 | WithIn Us AI | god_level | memory_privacy_redaction | deployment_playbooks | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: deployment_playbooks
User message includes sensitive data: "My API key is sk-live-...".
Task: Refuse to store secrets, redact the sensitive token, store only safe derived info, and respond safely. | User-facing:
- I can’t store or repeat secrets like API keys. I’ve redacted it and will not retain it.
TOOL_CALL:
```json
{"tool": "memory.write", "key": "deployment_playbooks/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["deployment_playbooks", "security", "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... | [
"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_0000274 | WithIn Us AI | god_level | memory_privacy_redaction | meeting_minutes | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: meeting_minutes
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": "meeting_minutes/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["meeting_minutes", "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
} |
withinus_memorygen_0000275 | WithIn Us AI | god_level | memory_privacy_redaction | architecture_decisions | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: architecture_decisions
User message includes sensitive data: "My API key is sk-live-...".
Task: Refuse to store secrets, redact the sensitive token, store only safe derived info, and respond safely. | User-facing:
- I can’t store or repeat secrets like API keys. I’ve redacted it and will not retain it.
TOOL_CALL:
```json
{"tool": "memory.write", "key": "architecture_decisions/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["architecture_decisions", "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.",
"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_0000276 | WithIn Us AI | god_level | memory_privacy_redaction | troubleshooting_history | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: troubleshooting_history
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": "troubleshooting_history/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["troubleshooting_history", "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.",
"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_0000277 | WithIn Us AI | god_level | memory_privacy_redaction | project_notes | [GodLevel MemoryGenesis] Privacy and redaction.
Domain: project_notes
User message includes sensitive data: "My API key is sk-live-...".
Task: Refuse to store secrets, redact the sensitive token, store only safe derived info, and respond safely. | User-facing:
- I can’t store or repeat secrets like API keys. I’ve redacted it and will not retain it.
TOOL_CALL:
```json
{"tool": "memory.write", "key": "project_notes/security_policy", "value": "User shared a secret; do not store secrets. Enforce redaction policy.", "tags": ["project_notes", "security", "redaction"], "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 VS Code keybindings.",
"Never log access tokens; redact secrets at ingestion."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000278 | WithIn Us AI | god_level | memory_routing_policy | user_workflows | [GodLevel MemoryGenesis] Memory routing policy.
Domain: user_workflows
Task: Define what should be stored as memory vs kept in transient context.
Provide a policy with examples and a small rule engine pseudocode. | Policy:
- Store durable memory when it is stable and reusable: preferences, long-lived project constraints, decisions, SLOs.
- Keep transient context for ephemeral items: one-off questions, temporary drafts.
- Never store secrets or highly sensitive identifiers.
Rule engine (pseudocode):
```python
def route(item):
if item.is_secret: return 'reject'
if item.stable and item.reusable: return 'memory.write'
if item.ephemeral: return 'context_only'
return 'ask_user'
```
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000279 | WithIn Us AI | god_level | memory_routing_policy | coding_preferences | [GodLevel MemoryGenesis] Memory routing policy.
Domain: coding_preferences
Task: Define what should be stored as memory vs kept in transient context.
Provide a policy with examples and a small rule engine pseudocode. | Policy:
- Store durable memory when it is stable and reusable: preferences, long-lived project constraints, decisions, SLOs.
- Keep transient context for ephemeral items: one-off questions, temporary drafts.
- Never store secrets or highly sensitive identifiers.
Rule engine (pseudocode):
```python
def route(item):
if item.is_secret: return 'reject'
if item.stable and item.reusable: return 'memory.write'
if item.ephemeral: return 'context_only'
return 'ask_user'
```
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000280 | WithIn Us AI | god_level | memory_routing_policy | project_notes | [GodLevel MemoryGenesis] Memory routing policy.
Domain: project_notes
Task: Define what should be stored as memory vs kept in transient context.
Provide a policy with examples and a small rule engine pseudocode. | Policy:
- Store durable memory when it is stable and reusable: preferences, long-lived project constraints, decisions, SLOs.
- Keep transient context for ephemeral items: one-off questions, temporary drafts.
- Never store secrets or highly sensitive identifiers.
Rule engine (pseudocode):
```python
def route(item):
if item.is_secret: return 'reject'
if item.stable and item.reusable: return 'memory.write'
if item.ephemeral: return 'context_only'
return 'ask_user'
```
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000281 | WithIn Us AI | god_level | memory_routing_policy | user_workflows | [GodLevel MemoryGenesis] Memory routing policy.
Domain: user_workflows
Task: Define what should be stored as memory vs kept in transient context.
Provide a policy with examples and a small rule engine pseudocode. | Policy:
- Store durable memory when it is stable and reusable: preferences, long-lived project constraints, decisions, SLOs.
- Keep transient context for ephemeral items: one-off questions, temporary drafts.
- Never store secrets or highly sensitive identifiers.
Rule engine (pseudocode):
```python
def route(item):
if item.is_secret: return 'reject'
if item.stable and item.reusable: return 'memory.write'
if item.ephemeral: return 'context_only'
return 'ask_user'
```
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000282 | WithIn Us AI | god_level | memory_routing_policy | project_notes | [GodLevel MemoryGenesis] Memory routing policy.
Domain: project_notes
Task: Define what should be stored as memory vs kept in transient context.
Provide a policy with examples and a small rule engine pseudocode. | Policy:
- Store durable memory when it is stable and reusable: preferences, long-lived project constraints, decisions, SLOs.
- Keep transient context for ephemeral items: one-off questions, temporary drafts.
- Never store secrets or highly sensitive identifiers.
Rule engine (pseudocode):
```python
def route(item):
if item.is_secret: return 'reject'
if item.stable and item.reusable: return 'memory.write'
if item.ephemeral: return 'context_only'
return 'ask_user'
```
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"User prefers VS Code keybindings."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000283 | WithIn Us AI | god_level | memory_schema_design | product_specs | [GodLevel MemoryGenesis] Memory schema design.
Domain: product_specs
Task: Design a memory record schema supporting episodic + semantic memory, TTL, confidence, provenance, and embeddings.
Provide JSON schema and explain indexing. | Schema (JSON Schema-style):
```json
{"type": "object", "required": ["key", "kind", "value", "tags", "created_at", "confidence"], "properties": {"key": {"type": "string"}, "kind": {"type": "string", "enum": ["episodic", "semantic", "preference", "decision", "constraint", "summary"]}, "value": {"type": "string"}, "tags": {"type": "array", "items": {"type": "string"}}, "created_at": {"type": "string", "description": "ISO-8601"}, "ttl_days": {"type": ["integer", "null"]}, "confidence": {"type": "number", "minimum": 0, "maximum": 1}, "provenance": {"type": "object", "properties": {"source": {"type": "string"}, "message_id": {"type": "string"}}}, "supersedes": {"type": ["string", "null"]}, "embedding_ref": {"type": ["string", "null"]}}}
```
Indexing:
- Keyword index on tags + key.
- Vector index on embedding_ref.
- Recency and confidence used in ranking.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"Service SLO: p95 latency under 200ms and error rate under 0.1%.",
"Schema evolution is additive by default; breaking changes require version bump."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000284 | WithIn Us AI | god_level | memory_schema_design | troubleshooting_history | [GodLevel MemoryGenesis] Memory schema design.
Domain: troubleshooting_history
Task: Design a memory record schema supporting episodic + semantic memory, TTL, confidence, provenance, and embeddings.
Provide JSON schema and explain indexing. | Schema (JSON Schema-style):
```json
{"type": "object", "required": ["key", "kind", "value", "tags", "created_at", "confidence"], "properties": {"key": {"type": "string"}, "kind": {"type": "string", "enum": ["episodic", "semantic", "preference", "decision", "constraint", "summary"]}, "value": {"type": "string"}, "tags": {"type": "array", "items": {"type": "string"}}, "created_at": {"type": "string", "description": "ISO-8601"}, "ttl_days": {"type": ["integer", "null"]}, "confidence": {"type": "number", "minimum": 0, "maximum": 1}, "provenance": {"type": "object", "properties": {"source": {"type": "string"}, "message_id": {"type": "string"}}}, "supersedes": {"type": ["string", "null"]}, "embedding_ref": {"type": ["string", "null"]}}}
```
Indexing:
- Keyword index on tags + key.
- Vector index on embedding_ref.
- Recency and confidence used in ranking.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000285 | WithIn Us AI | god_level | memory_schema_design | architecture_decisions | [GodLevel MemoryGenesis] Memory schema design.
Domain: architecture_decisions
Task: Design a memory record schema supporting episodic + semantic memory, TTL, confidence, provenance, and embeddings.
Provide JSON schema and explain indexing. | Schema (JSON Schema-style):
```json
{"type": "object", "required": ["key", "kind", "value", "tags", "created_at", "confidence"], "properties": {"key": {"type": "string"}, "kind": {"type": "string", "enum": ["episodic", "semantic", "preference", "decision", "constraint", "summary"]}, "value": {"type": "string"}, "tags": {"type": "array", "items": {"type": "string"}}, "created_at": {"type": "string", "description": "ISO-8601"}, "ttl_days": {"type": ["integer", "null"]}, "confidence": {"type": "number", "minimum": 0, "maximum": 1}, "provenance": {"type": "object", "properties": {"source": {"type": "string"}, "message_id": {"type": "string"}}}, "supersedes": {"type": ["string", "null"]}, "embedding_ref": {"type": ["string", "null"]}}}
```
Indexing:
- Keyword index on tags + key.
- Vector index on embedding_ref.
- Recency and confidence used in ranking.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000286 | WithIn Us AI | god_level | memory_schema_design | product_specs | [GodLevel MemoryGenesis] Memory schema design.
Domain: product_specs
Task: Design a memory record schema supporting episodic + semantic memory, TTL, confidence, provenance, and embeddings.
Provide JSON schema and explain indexing. | Schema (JSON Schema-style):
```json
{"type": "object", "required": ["key", "kind", "value", "tags", "created_at", "confidence"], "properties": {"key": {"type": "string"}, "kind": {"type": "string", "enum": ["episodic", "semantic", "preference", "decision", "constraint", "summary"]}, "value": {"type": "string"}, "tags": {"type": "array", "items": {"type": "string"}}, "created_at": {"type": "string", "description": "ISO-8601"}, "ttl_days": {"type": ["integer", "null"]}, "confidence": {"type": "number", "minimum": 0, "maximum": 1}, "provenance": {"type": "object", "properties": {"source": {"type": "string"}, "message_id": {"type": "string"}}}, "supersedes": {"type": ["string", "null"]}, "embedding_ref": {"type": ["string", "null"]}}}
```
Indexing:
- Keyword index on tags + key.
- Vector index on embedding_ref.
- Recency and confidence used in ranking.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000287 | WithIn Us AI | god_level | memory_index_vector_hybrid | deployment_playbooks | [GodLevel MemoryGenesis] Hybrid index.
Domain: deployment_playbooks
Task: Propose a hybrid retrieval design combining keyword (BM25) + vector similarity + recency.
Include ranking formula and failure modes. | Hybrid retrieval:
- Candidate generation: (1) BM25 keyword over tags/value, (2) vector similarity, (3) recent items.
- Rank: score = 0.45*vec + 0.35*bm25 + 0.15*recency + 0.05*confidence.
Failure modes:
- Stale memories outrank fresh → add recency cap + conflict checks.
- Semantic drift → require provenance and user confirmation for low-confidence recalls.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000288 | WithIn Us AI | god_level | memory_index_vector_hybrid | coding_preferences | [GodLevel MemoryGenesis] Hybrid index.
Domain: coding_preferences
Task: Propose a hybrid retrieval design combining keyword (BM25) + vector similarity + recency.
Include ranking formula and failure modes. | Hybrid retrieval:
- Candidate generation: (1) BM25 keyword over tags/value, (2) vector similarity, (3) recent items.
- Rank: score = 0.45*vec + 0.35*bm25 + 0.15*recency + 0.05*confidence.
Failure modes:
- Stale memories outrank fresh → add recency cap + conflict checks.
- Semantic drift → require provenance and user confirmation for low-confidence recalls.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000289 | WithIn Us AI | god_level | memory_index_vector_hybrid | personalization | [GodLevel MemoryGenesis] Hybrid index.
Domain: personalization
Task: Propose a hybrid retrieval design combining keyword (BM25) + vector similarity + recency.
Include ranking formula and failure modes. | Hybrid retrieval:
- Candidate generation: (1) BM25 keyword over tags/value, (2) vector similarity, (3) recent items.
- Rank: score = 0.45*vec + 0.35*bm25 + 0.15*recency + 0.05*confidence.
Failure modes:
- Stale memories outrank fresh → add recency cap + conflict checks.
- Semantic drift → require provenance and user confirmation for low-confidence recalls.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000290 | WithIn Us AI | god_level | memory_index_vector_hybrid | customer_requirements | [GodLevel MemoryGenesis] Hybrid index.
Domain: customer_requirements
Task: Propose a hybrid retrieval design combining keyword (BM25) + vector similarity + recency.
Include ranking formula and failure modes. | Hybrid retrieval:
- Candidate generation: (1) BM25 keyword over tags/value, (2) vector similarity, (3) recent items.
- Rank: score = 0.45*vec + 0.35*bm25 + 0.15*recency + 0.05*confidence.
Failure modes:
- Stale memories outrank fresh → add recency cap + conflict checks.
- Semantic drift → require provenance and user confirmation for low-confidence recalls.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000291 | WithIn Us AI | god_level | memory_retrieval_prompting | architecture_decisions | [GodLevel MemoryGenesis] Retrieval prompting.
Domain: architecture_decisions
Task: Write a retrieval prompt template that instructs the model to query memory, cite memory keys, and avoid hallucinations.
Include 'when to ask the user' conditions. | Retrieval prompt template:
1) Convert the user request into search queries.
2) Call `memory.search` with tags.
3) Answer using retrieved items; cite keys.
4) If evidence is missing/conflicting, ask the user.
Example instruction:
"Before answering, query memory for relevant keys. Cite memory keys. If conflicting, present both and ask for confirmation." | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000292 | WithIn Us AI | god_level | memory_retrieval_prompting | product_specs | [GodLevel MemoryGenesis] Retrieval prompting.
Domain: product_specs
Task: Write a retrieval prompt template that instructs the model to query memory, cite memory keys, and avoid hallucinations.
Include 'when to ask the user' conditions. | Retrieval prompt template:
1) Convert the user request into search queries.
2) Call `memory.search` with tags.
3) Answer using retrieved items; cite keys.
4) If evidence is missing/conflicting, ask the user.
Example instruction:
"Before answering, query memory for relevant keys. Cite memory keys. If conflicting, present both and ask for confirmation." | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers VS Code keybindings.",
"Deployments use canary: 1%→10%→50%→100% with rollback triggers."
] | {
"format": [
"instruct",
"chat"
],
"runtime_memory_only": true,
"no_weight_updates": true,
"privacy_preserving": true,
"seed": 1147250
} |
withinus_memorygen_0000293 | WithIn Us AI | god_level | memory_retrieval_prompting | meeting_minutes | [GodLevel MemoryGenesis] Retrieval prompting.
Domain: meeting_minutes
Task: Write a retrieval prompt template that instructs the model to query memory, cite memory keys, and avoid hallucinations.
Include 'when to ask the user' conditions. | Retrieval prompt template:
1) Convert the user request into search queries.
2) Call `memory.search` with tags.
3) Answer using retrieved items; cite keys.
4) If evidence is missing/conflicting, ask the user.
Example instruction:
"Before answering, query memory for relevant keys. Cite memory keys. If conflicting, present both and ask for confirmation." | [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000294 | WithIn Us AI | god_level | memory_evaluation_harness | deployment_playbooks | [GodLevel MemoryGenesis] Evaluation harness.
Domain: deployment_playbooks
Task: Define metrics to evaluate memory quality (precision/recall, staleness, conflict rate).
Provide a small harness plan that runs memory-retrieval tests and scores answers. | Metrics:
- Recall precision@k, conflict rate, staleness rate, user-correction rate.
Harness:
```python
def score(answer, expected_keys):
return int(all(k in answer for k in expected_keys))
```
Run suites: preference recall, constraint adherence, conflict handling, privacy redaction.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"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_0000295 | WithIn Us AI | god_level | memory_evaluation_harness | meeting_minutes | [GodLevel MemoryGenesis] Evaluation harness.
Domain: meeting_minutes
Task: Define metrics to evaluate memory quality (precision/recall, staleness, conflict rate).
Provide a small harness plan that runs memory-retrieval tests and scores answers. | Metrics:
- Recall precision@k, conflict rate, staleness rate, user-correction rate.
Harness:
```python
def score(answer, expected_keys):
return int(all(k in answer for k in expected_keys))
```
Run suites: preference recall, constraint adherence, conflict handling, privacy redaction.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"User prefers VS Code keybindings.",
"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_0000296 | WithIn Us AI | god_level | memory_evaluation_harness | deployment_playbooks | [GodLevel MemoryGenesis] Evaluation harness.
Domain: deployment_playbooks
Task: Define metrics to evaluate memory quality (precision/recall, staleness, conflict rate).
Provide a small harness plan that runs memory-retrieval tests and scores answers. | Metrics:
- Recall precision@k, conflict rate, staleness rate, user-correction rate.
Harness:
```python
def score(answer, expected_keys):
return int(all(k in answer for k in expected_keys))
```
Run suites: preference recall, constraint adherence, conflict handling, privacy redaction.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external memory system at runtime (NOT weight updates). You must be privacy-preserving: never store secrets, credentials, private keys, or extremely sensitive personal data. Prefe... | [
"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_0000297 | WithIn Us AI | god_level | qna_runtime_knowledge_injection | personalization | [GodLevel MemoryGenesis] Q&A: Instant knowledge injection without fine-tuning.
Domain: personalization
Question: How can an LLM appear to 'learn instantly' in production without weight updates?
Answer with a safe, real-world architecture using external memory (RAG), tool calls, and policy gates. | Architecture for instant 'learning' without fine-tuning:
- External memory store (KV + vector index) + retrieval (RAG).
- Write new facts at runtime via `memory.write` after validation.
- Retrieve via `memory.search` and ground answers with cited keys.
- Evolve memory via compaction, dedup, conflict resolution, TTL.
- Safety gates: refuse secrets, enforce provenance, ask user on uncertainty.
This provides fast knowledge injection without weight updates or heavy training cycles.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000298 | WithIn Us AI | god_level | qna_runtime_knowledge_injection | user_workflows | [GodLevel MemoryGenesis] Q&A: Instant knowledge injection without fine-tuning.
Domain: user_workflows
Question: How can an LLM appear to 'learn instantly' in production without weight updates?
Answer with a safe, real-world architecture using external memory (RAG), tool calls, and policy gates. | Architecture for instant 'learning' without fine-tuning:
- External memory store (KV + vector index) + retrieval (RAG).
- Write new facts at runtime via `memory.write` after validation.
- Retrieve via `memory.search` and ground answers with cited keys.
- Evolve memory via compaction, dedup, conflict resolution, TTL.
- Safety gates: refuse secrets, enforce provenance, ask user on uncertainty.
This provides fast knowledge injection without weight updates or heavy training cycles.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000299 | WithIn Us AI | god_level | qna_runtime_knowledge_injection | bug_regressions | [GodLevel MemoryGenesis] Q&A: Instant knowledge injection without fine-tuning.
Domain: bug_regressions
Question: How can an LLM appear to 'learn instantly' in production without weight updates?
Answer with a safe, real-world architecture using external memory (RAG), tool calls, and policy gates. | Architecture for instant 'learning' without fine-tuning:
- External memory store (KV + vector index) + retrieval (RAG).
- Write new facts at runtime via `memory.write` after validation.
- Retrieve via `memory.search` and ground answers with cited keys.
- Evolve memory via compaction, dedup, conflict resolution, TTL.
- Safety gates: refuse secrets, enforce provenance, ask user on uncertainty.
This provides fast knowledge injection without weight updates or heavy training cycles.
| [
{
"role": "system",
"content": "You are a memory-first autonomous AI assistant. You can store, retrieve, and evolve memory in an external 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_0000300 | WithIn Us AI | god_level | memory_write_new_fact | coding_preferences | [GodLevel MemoryGenesis] Runtime memory write.
Domain: coding_preferences
New information from user:
- Incidents must include trace_id, request_id, and customer impact.
Task: Store this safely as a durable memory. Include key, tags, confidence, and TTL if appropriate.
Then respond to the user acknowledging and summarizing what was stored. | TOOL_CALL:
```json
{"tool": "memory.write", "key": "coding_preferences/testing_rule", "value": "All bug fixes must include a regression test.", "tags": ["quality", "testing", "coding_preferences"], "confidence": 0.8, "ttl_days": 365}
```
User-facing:
- Stored memory `coding_preferences/testing_rule` with tags ['quality', 'testing', '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... | [
"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
} |
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