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8 results for “mmornati”
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Follow-up to my Jev × Home Assistant post: two comments on the last one deserved a real answer, not just a reply.
@oneclickclaw_io asked why not feed Jev's context to a small local model instead. Fair point - my prompts really were just snapshots (humidity *now*, flow *now*), no memory of what led there.
@nocalla asked whether that's not exactly what the Bayesian sensor is for.
Both got tested, not just answered:
→ Why not local: the box is an N100 mini PC, 4 cores, 16 GB RAM, no GPU, already running HA Core + recorder + Zigbee2MQTT. A local model's own numbers (from the router benchmark) put it at 1.5-3s/call on that class of hardware, several times an hour, contending with real-time Zigbee. And the actual gap was never model intelligence - Jev has a 32k-token window and was only getting ~565 tokens of *current* facts.
→ So instead: a compact-history script built on Home Assistant's own recorder statistics, a 24h house timeline, per-room humidity baselines, a broken-sensor safety net (turns out two humidity sensors had been stuck at 0% - again), per-decision shadow/active switches, and a feedback loop - "who was right?" on the phone, scored on a scoreboard, summarised every week.
→ Bayesian sensor: close, but not the same tool. You hand-estimate every probability yourself, it's strictly on/off, treats correlated clues as independent, and has no idea when its own inputs are lying. Jev takes plain facts, returns a full distribution over N options, and reasons over the whole state. Bayesian still wins for signals you already understand and want fully offline (occupancy, say) - and the two combine nicely.
Real production numbers from the first days: 267 calls, ~206k tokens, $0.0087 that day. And a live finding I didn't expect: the VMC decision disagrees with its own rule 14/14 times at low confidence, while the shutters decision agrees 78/78 times - exactly why the per-decision switch earns its keep.
Full write-up:
https://blog.mornati.net/jev-home-assistant-history-feedback-and-the-local-model-question/#HomeAssistant #SmartHome #HomeAutomation #AI #LLM #SelfHosted
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Follow-up to my Jev × Home Assistant post: two comments on the last one deserved a real answer, not just a reply.
@oneclickclaw_io asked why not feed Jev's context to a small local model instead. Fair point - my prompts really were just snapshots (humidity *now*, flow *now*), no memory of what led there.
@nocalla asked whether that's not exactly what the Bayesian sensor is for.
Both got tested, not just answered:
→ Why not local: the box is an N100 mini PC, 4 cores, 16 GB RAM, no GPU, already running HA Core + recorder + Zigbee2MQTT. A local model's own numbers (from the router benchmark) put it at 1.5-3s/call on that class of hardware, several times an hour, contending with real-time Zigbee. And the actual gap was never model intelligence - Jev has a 32k-token window and was only getting ~565 tokens of *current* facts.
→ So instead: a compact-history script built on Home Assistant's own recorder statistics, a 24h house timeline, per-room humidity baselines, a broken-sensor safety net (turns out two humidity sensors had been stuck at 0% - again), per-decision shadow/active switches, and a feedback loop - "who was right?" on the phone, scored on a scoreboard, summarised every week.
→ Bayesian sensor: close, but not the same tool. You hand-estimate every probability yourself, it's strictly on/off, treats correlated clues as independent, and has no idea when its own inputs are lying. Jev takes plain facts, returns a full distribution over N options, and reasons over the whole state. Bayesian still wins for signals you already understand and want fully offline (occupancy, say) - and the two combine nicely.
Real production numbers from the first days: 267 calls, ~206k tokens, $0.0087 that day. And a live finding I didn't expect: the VMC decision disagrees with its own rule 14/14 times at low confidence, while the shutters decision agrees 78/78 times - exactly why the per-decision switch earns its keep.
Full write-up:
https://blog.mornati.net/jev-home-assistant-history-feedback-and-the-local-model-question/#HomeAssistant #SmartHome #HomeAutomation #AI #LLM #SelfHosted
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Follow-up to my Jev × Home Assistant post: two comments on the last one deserved a real answer, not just a reply.
@oneclickclaw_io asked why not feed Jev's context to a small local model instead. Fair point - my prompts really were just snapshots (humidity *now*, flow *now*), no memory of what led there.
@nocalla asked whether that's not exactly what the Bayesian sensor is for.
Both got tested, not just answered:
→ Why not local: the box is an N100 mini PC, 4 cores, 16 GB RAM, no GPU, already running HA Core + recorder + Zigbee2MQTT. A local model's own numbers (from the router benchmark) put it at 1.5-3s/call on that class of hardware, several times an hour, contending with real-time Zigbee. And the actual gap was never model intelligence - Jev has a 32k-token window and was only getting ~565 tokens of *current* facts.
→ So instead: a compact-history script built on Home Assistant's own recorder statistics, a 24h house timeline, per-room humidity baselines, a broken-sensor safety net (turns out two humidity sensors had been stuck at 0% - again), per-decision shadow/active switches, and a feedback loop - "who was right?" on the phone, scored on a scoreboard, summarised every week.
→ Bayesian sensor: close, but not the same tool. You hand-estimate every probability yourself, it's strictly on/off, treats correlated clues as independent, and has no idea when its own inputs are lying. Jev takes plain facts, returns a full distribution over N options, and reasons over the whole state. Bayesian still wins for signals you already understand and want fully offline (occupancy, say) - and the two combine nicely.
Real production numbers from the first days: 267 calls, ~206k tokens, $0.0087 that day. And a live finding I didn't expect: the VMC decision disagrees with its own rule 14/14 times at low confidence, while the shutters decision agrees 78/78 times - exactly why the per-decision switch earns its keep.
Full write-up:
https://blog.mornati.net/jev-home-assistant-history-feedback-and-the-local-model-question/#HomeAssistant #SmartHome #HomeAutomation #AI #LLM #SelfHosted
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My home automations know thresholds, not context.
Sump pump runs 2 min after a rainy night → alert.
Long shower → "possible leak".
Washer pauses to soak → "finished", 20 min too early.So I gave the "is this normal?" question to Jev, TypeSafe's decision model. You send it a few lines of facts and a typed question, and it answers with a calibrated probability. No free text, no JSON to parse.
How it's wired into Home Assistant:
• The old rule always computes its answer. Jev only advises.
• One switch: shadow (log only), active, or off.
• An error, a spent budget or low confidence → the rule wins, so the alert still fires.
• 6 automations: sump pump, water, temperature drops, shutters, ventilation, laundry.Cost: 133 calls and 75k tokens on day one, about $3 a year for the whole house.
Two lessons that aren't about AI:
1. Writing the facts for the model made me read them. Two humidity sensors had been stuck at 0 %, and my old rules were using them.
2. My GitOps sync had been failing silently for 5 weeks. Merged ≠ deployed.Also in the post: can Laya do the same job locally, to keep occupancy data at home?
https://blog.mornati.net/jev-home-assistant-letting-a-decision-model-judge-my-automations/
#HomeAssistant #SmartHome #HomeAutomation #LLM #AI #SelfHosted
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My home automations know thresholds, not context.
Sump pump runs 2 min after a rainy night → alert.
Long shower → "possible leak".
Washer pauses to soak → "finished", 20 min too early.So I gave the "is this normal?" question to Jev, TypeSafe's decision model. You send it a few lines of facts and a typed question, and it answers with a calibrated probability. No free text, no JSON to parse.
How it's wired into Home Assistant:
• The old rule always computes its answer. Jev only advises.
• One switch: shadow (log only), active, or off.
• An error, a spent budget or low confidence → the rule wins, so the alert still fires.
• 6 automations: sump pump, water, temperature drops, shutters, ventilation, laundry.Cost: 133 calls and 75k tokens on day one, about $3 a year for the whole house.
Two lessons that aren't about AI:
1. Writing the facts for the model made me read them. Two humidity sensors had been stuck at 0 %, and my old rules were using them.
2. My GitOps sync had been failing silently for 5 weeks. Merged ≠ deployed.Also in the post: can Laya do the same job locally, to keep occupancy data at home?
https://blog.mornati.net/jev-home-assistant-letting-a-decision-model-judge-my-automations/
#HomeAssistant #SmartHome #HomeAutomation #LLM #AI #SelfHosted
-
My home automations know thresholds, not context.
Sump pump runs 2 min after a rainy night → alert.
Long shower → "possible leak".
Washer pauses to soak → "finished", 20 min too early.So I gave the "is this normal?" question to Jev, TypeSafe's decision model. You send it a few lines of facts and a typed question, and it answers with a calibrated probability. No free text, no JSON to parse.
How it's wired into Home Assistant:
• The old rule always computes its answer. Jev only advises.
• One switch: shadow (log only), active, or off.
• An error, a spent budget or low confidence → the rule wins, so the alert still fires.
• 6 automations: sump pump, water, temperature drops, shutters, ventilation, laundry.Cost: 133 calls and 75k tokens on day one, about $3 a year for the whole house.
Two lessons that aren't about AI:
1. Writing the facts for the model made me read them. Two humidity sensors had been stuck at 0 %, and my old rules were using them.
2. My GitOps sync had been failing silently for 5 weeks. Merged ≠ deployed.Also in the post: can Laya do the same job locally, to keep occupancy data at home?
https://blog.mornati.net/jev-home-assistant-letting-a-decision-model-judge-my-automations/
#HomeAssistant #SmartHome #HomeAutomation #LLM #AI #SelfHosted
-
My home automations know thresholds, not context.
Sump pump runs 2 min after a rainy night → alert.
Long shower → "possible leak".
Washer pauses to soak → "finished", 20 min too early.So I gave the "is this normal?" question to Jev, TypeSafe's decision model. You send it a few lines of facts and a typed question, and it answers with a calibrated probability. No free text, no JSON to parse.
How it's wired into Home Assistant:
• The old rule always computes its answer. Jev only advises.
• One switch: shadow (log only), active, or off.
• An error, a spent budget or low confidence → the rule wins, so the alert still fires.
• 6 automations: sump pump, water, temperature drops, shutters, ventilation, laundry.Cost: 133 calls and 75k tokens on day one, about $3 a year for the whole house.
Two lessons that aren't about AI:
1. Writing the facts for the model made me read them. Two humidity sensors had been stuck at 0 %, and my old rules were using them.
2. My GitOps sync had been failing silently for 5 weeks. Merged ≠ deployed.Also in the post: can Laya do the same job locally, to keep occupancy data at home?
https://blog.mornati.net/jev-home-assistant-letting-a-decision-model-judge-my-automations/
#HomeAssistant #SmartHome #HomeAutomation #LLM #AI #SelfHosted
-
My home automations know thresholds, not context.
Sump pump runs 2 min after a rainy night → alert.
Long shower → "possible leak".
Washer pauses to soak → "finished", 20 min too early.So I gave the "is this normal?" question to Jev, TypeSafe's decision model. You send it a few lines of facts and a typed question, and it answers with a calibrated probability. No free text, no JSON to parse.
How it's wired into Home Assistant:
• The old rule always computes its answer. Jev only advises.
• One switch: shadow (log only), active, or off.
• An error, a spent budget or low confidence → the rule wins, so the alert still fires.
• 6 automations: sump pump, water, temperature drops, shutters, ventilation, laundry.Cost: 133 calls and 75k tokens on day one, about $3 a year for the whole house.
Two lessons that aren't about AI:
1. Writing the facts for the model made me read them. Two humidity sensors had been stuck at 0 %, and my old rules were using them.
2. My GitOps sync had been failing silently for 5 weeks. Merged ≠ deployed.Also in the post: can Laya do the same job locally, to keep occupancy data at home?
https://blog.mornati.net/jev-home-assistant-letting-a-decision-model-judge-my-automations/
#HomeAssistant #SmartHome #HomeAutomation #LLM #AI #SelfHosted