sync: record magatama multi-llm training lanes
This commit is contained in:
Rene Fichtmueller
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635a102932
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3926a1ef90
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# Current TIP Sync State
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# Current TIP Sync State
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Updated: 2026-05-09 22:01 UTC
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Updated: 2026-05-09 22:05 UTC
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## Newest Work
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## Newest Work
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- MAGATAMA multi-LLM training lane expansion on 2026-05-09:
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- added first-class training lanes for:
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- `pulso_llm`
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- `contact_llm`
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- MAGATAMA training tool now exposes:
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- `MagatamaLLM`
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- `FO_BlogLLM`
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- `TIP_LLM`
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- `PulsoLLM`
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- `ContactLLM`
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- lane split is now canonical:
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- `MagatamaLLM`: MAGATAMA operations, cybersecurity, AI security, infrastructure security, resolver/fix workflows
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- `FO_BlogLLM`: Rene/Flexoptix-style blog writing, technical storytelling, market/blog structure
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- `TIP_LLM`: crawler/scraper/robot planning, source discovery, parser/selectors, switch/transceiver issue research
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- `PulsoLLM`: Flexoptix product/support/diagnostic lane for switches, transceivers, compatibility, product fit and offers
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- `ContactLLM`: contact discovery/research lane for structured, lawful contact lookup and source attribution
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- shared network/transceiver/switch knowledge is intentionally reused for `TIP_LLM` and `PulsoLLM`, but behavior/instruction pools remain separate
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- new source catalog added under MAGATAMA:
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- `training-data/model-registry/research-source-catalog-2026-05-09.json`
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- `training-data/model-registry/external-ingest/llm-lane-research-seeds-2026-05-09.jsonl`
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- source seeds added from current research include:
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- CISA KEV / CISA Malcolm / CISA ScubaGear
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- NVD CVE API
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- MITRE ATT&CK STIX/TAXII
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- OWASP LLM Top 10
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- Microsoft PyRIT
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- Microsoft Agent Governance Toolkit
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- Cisco Transceiver Module Group matrix
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- Juniper Hardware Compatibility Tool
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- Arista transceiver/cable references
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- Flexoptix product/support references
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- RFC 9309 robots.txt
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- schema.org `ContactPoint`
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- RFC 6350 vCard
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- PeeringDB API
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- RIPE Database REST API
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- lane-specific Gitea learning pool directories now exist for:
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- `training-data/gitea-learning-pool/pulso_llm/`
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- `training-data/gitea-learning-pool/contact_llm/`
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- RunPod lane exports rebuilt and deployed live on Erik:
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- `magatamallm`: `1375 train`, `153 eval`, `1528 total`
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- `fo_blogllm`: `17342 train`, `1929 eval`, `19271 total`
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- `tip_llm`: `276 train`, `31 eval`, `307 total`
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- `pulso_llm`: `28 train`, `5 eval`, `33 total`
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- `contact_llm`: `18 train`, `4 eval`, `22 total`
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- dashboard/API live checks:
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- `pulso_llm` and `contact_llm` appear in the training modal
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- RunPod provider is online for both lanes
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- `contact_llm` status correctly reports `neverTrained: true`
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- `pulso_llm` / `contact_llm` are trainable but not adopted yet because no local Ollama model tags exist yet
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- safety/automation note:
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- do not mark a lane training run successful unless an artifact exists, imports locally, passes smoke tests, and the active alias/version is switched
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- this remains the rule for all LLM lanes
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- TIP open competitor status closure on 2026-05-09:
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- TIP open competitor status closure on 2026-05-09:
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- added migration `sql/104-verification-evidence-ambiguous.sql`
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- added migration `sql/104-verification-evidence-ambiguous.sql`
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- extends `transceiver_verification_evidence.verification_type` with `competitor_ambiguous`
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- extends `transceiver_verification_evidence.verification_type` with `competitor_ambiguous`
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86
sync/history/2026-05-09-magatama-multi-llm-training-lanes.md
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sync/history/2026-05-09-magatama-multi-llm-training-lanes.md
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@ -0,0 +1,86 @@
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# MAGATAMA Multi-LLM Training Lanes
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Date: 2026-05-09
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## Decision
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MAGATAMA now treats the core specialized models as separate training lanes with separate behavior pools:
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- `magatamallm`: MAGATAMA operations, cybersecurity, AI security, infrastructure security, resolver/fix workflows.
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- `fo_blogllm`: Rene/Flexoptix-style blog writing, technical storytelling, market/blog structure.
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- `tip_llm`: crawler/scraper/robot planning, source discovery, parser/selectors, switch/transceiver issue research.
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- `pulso_llm`: Flexoptix product/support/diagnostic lane for switches, transceivers, compatibility, product fit and offers.
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- `contact_llm`: structured, lawful contact discovery/research with attribution.
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`TIP_LLM` and `PulsoLLM` share a network/transceiver/switch knowledge core, but their instruction and behavior pools stay separate.
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## Implemented In MAGATAMA
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- Added `pulso_llm` and `contact_llm` to:
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- RunPod dataset builder
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- Gitea training pool sync
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- model registry build
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- RunPod submit path
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- HuggingFace/RunPod dataset publishing config
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- Susan/NAS training scan
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- full training refresh
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- dashboard training API/status
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- training modal UI
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- fine-tuner lane config and smoke prompts
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- Added new lane profiles:
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- `PulsoLLM`
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- `ContactLLM`
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- Added source catalog:
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- `training-data/model-registry/research-source-catalog-2026-05-09.json`
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- `training-data/model-registry/external-ingest/llm-lane-research-seeds-2026-05-09.jsonl`
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- Added Gitea-backed seed pools:
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- `training-data/gitea-learning-pool/pulso_llm/`
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- `training-data/gitea-learning-pool/contact_llm/`
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## Source Seeds Added
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- CISA KEV / CISA Malcolm / CISA ScubaGear
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- NVD CVE API
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- MITRE ATT&CK STIX/TAXII
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- OWASP LLM Top 10
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- Microsoft PyRIT
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- Microsoft Agent Governance Toolkit
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- Cisco Transceiver Module Group matrix
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- Juniper Hardware Compatibility Tool
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- Arista transceiver/cable references
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- Flexoptix product/support references
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- RFC 9309 robots.txt
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- schema.org `ContactPoint`
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- RFC 6350 vCard
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- PeeringDB API
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- RIPE Database REST API
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## Verified Counts
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RunPod lane exports rebuilt and deployed live on Erik:
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- `magatamallm`: `1375 train`, `153 eval`, `1528 total`
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- `fo_blogllm`: `17342 train`, `1929 eval`, `19271 total`
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- `tip_llm`: `276 train`, `31 eval`, `307 total`
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- `pulso_llm`: `28 train`, `5 eval`, `33 total`
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- `contact_llm`: `18 train`, `4 eval`, `22 total`
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## Live Verification
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- `pulso_llm` and `contact_llm` appear in the MAGATAMA training modal.
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- RunPod provider is online for both new lanes.
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- `contact_llm` status correctly reports `neverTrained: true`.
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- `pulso_llm` / `contact_llm` are trainable but not adopted yet because no local Ollama model tags exist yet.
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## Operational Rule
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Training success only counts when all of the following are true:
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- RunPod reports completion.
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- An artifact exists and is reachable.
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- Local import succeeds.
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- Smoke tests pass.
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- The active alias/version is switched.
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- The registry and dashboard show the new version.
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If any part fails, the lane must stay in a non-adopted state.
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