Setting Up and Monitoring ElphaPex DG Home1 on PowerPool Using Home Assistant

Introduction

Home Assistant is an excellent tool for monitoring the ElphaPex DG Home1 miner. This post covers my observations on optimizing its setup for maximum efficiency with minimal downtime.

PowerPool Configuration

Setting up PowerPool is straightforward. You need to enter the PowerPool stratum address three times. This works because PowerPool addresses have multiple DNS entries. If one connection fails, the miner automatically connects to the next available address.

Configuration Example

When the first active entry fails, the miner switches to the next, as shown in the ElphaPex logs:

Feb 17 08:49:34 DG-Home1 user.info health: min = 44250 max = 49812
Feb 17 08:49:49 DG-Home1 user.err cpuminer[1682]: [ttyS5][Stratum connection timed out]
Feb 17 08:49:49 DG-Home1 user.notice cpuminer[1682]: [ttyS5][current pool: stratum+tcp://scrypt.stratum.powerpool.io:3333, user: elsonico.dg1, pass: x]
Feb 17 08:49:49 DG-Home1 user.err cpuminer[1682]: [ttyS5][Stratum connection interrupted]
Feb 17 08:49:49 DG-Home1 user.info cpuminer[1682]: [ttyS5][Starting Stratum on stratum+tcp://scrypt.stratum.powerpool.io:3333]

When a connection fails, the previous pool becomes “Unreachable,” the second “Alive,” and the third remains “Standby.”

This setup works with any mining pool that has multiple resolvable IP addresses. You can verify DNS resolution using:

🕙 11:07:57 ❯ nslookup scrypt.stratum.powerpool.io
Server: 192.168.0.210
Address: 192.168.0.210#53
Non-authoritative answer:
scrypt.stratum.powerpool.io canonical name = scrypt.powerpool.cloud.
Name: scrypt.powerpool.cloud
Address: 161.97.162.191
Name: scrypt.powerpool.cloud
Address: 149.102.159.179
Name: scrypt.powerpool.cloud
Address: 75.119.136.155
Name: scrypt.powerpool.cloud
Address: 158.220.80.69

Monitoring

Hash Rate Monitoring

A low hash rate over an extended period indicates a malfunction. Initially, I monitored the hash rate via PowerPool’s API but noticed occasional inaccuracies. To address this, I now monitor the miner’s own reported hash rate alongside PowerPool’s API data.

If the hash rate stays at 0.0 for 15 minutes, Home Assistant automatically reboots the miner using the following automation:

- id: 28077b7f639644688b946f35ccf0127b
  alias: Reboot or Power Cycle Miner
  description: Reboot the miner on low hash rate, fallback to power cycle if needed
  triggers:
  - entity_id: sensor.miner_hashrate_gh
    to: '0.0'
    for:   
      minutes: 15
    trigger: state
  - minutes: /15 
    trigger: time_pattern
  conditions:
  - condition: numeric_state
    entity_id: sensor.miner_hashrate_gh
    below: 0.1
  - condition: numeric_state
    entity_id: sensor.electricity_cost_in_cents_per_kwh
    below: 62
  actions: 
  - target:
      entity_id: switch.shellyplug_s_3ce90ee04b97
    action: switch.turn_off
    data: {}
  - delay: 
      seconds: 10
  - target:
      entity_id: switch.shellyplug_s_3ce90ee04b97
    action: switch.turn_on
    data: {}
  mode: single

Note that I only do this when the electricity price is below 62c/kWh. I will go into details later why that has been set the way it has.

Temperature Monitoring

I monitor various circuit temperatures. If any exceed 55°C, an event triggers notifications via email and WhatsApp, giving me time to intervene before an automatic shutdown.

Example of WhatsApp message when any of them go above 56C:

Above is done with following snippet on my automations.yaml:

- id: miner_whats_app_temperature_alert
  alias: WhatsApp alert when miner temperatures are high
  trigger:
    platform: numeric_state
    entity_id:
    - sensor.miner_outlet_temperature_1
    - sensor.miner_outlet_temperature_2
    - sensor.miner_inlet_temperature_1
    - sensor.miner_inlet_temperature_2
    above: 55
  condition:
  - condition: template
    value_template: "{% set temps = namespace(high=false) %} {% for sensor in [\n
      \    'sensor.miner_outlet_temperature_1', \n     'sensor.miner_outlet_temperature_2',
      \n     'sensor.miner_inlet_temperature_1', \n     'sensor.miner_inlet_temperature_2'\n
      \  ] %}\n  {% if states(sensor) not in ['unknown', 'unavailable'] %}\n    {%
      if states(sensor) | float > 55 %}\n      {% set temps.high = true %}\n    {%
      endif %}\n  {% endif %}\n{% endfor %} {{ temps.high }}\n"
  mode: single
  action:
  - service: persistent_notification.create
    data:
      title: 'Debug: Miner Temperature Alert Triggered'
      message: 'Current temperatures: Outlet 1: {{ states(''sensor.miner_outlet_temperature_1'')
        }}°C, Outlet 2: {{ states(''sensor.miner_outlet_temperature_2'') }}°C, Inlet
        1: {{ states(''sensor.miner_inlet_temperature_1'') }}°C, Inlet 2: {{ states(''sensor.miner_inlet_temperature_2'')
        }}°C'
  - service: notify.email_notify
    data:
      title: Miner Alert - High Temperature
      message: 'High temperature detected on your miner: Outlet 1: {{ states(''sensor.miner_outlet_temperature_1'')
        }}°C, Outlet 2: {{ states(''sensor.miner_outlet_temperature_2'') }}°C, Inlet
        1: {{ states(''sensor.miner_inlet_temperature_1'') }}°C, Inlet 2: {{ states(''sensor.miner_inlet_temperature_2'')
        }}°C. Please check your miner''s cooling system.'
  - service: notify.whatsapp
    data:
      title: Miner Alert - High Temperature
      message: 'High temperature detected on your miner: Outlet 1: {{ states(''sensor.miner_outlet_temperature_1'')
        }}°C, Outlet 2: {{ states(''sensor.miner_outlet_temperature_2'') }}°C, Inlet
        1: {{ states(''sensor.miner_inlet_temperature_1'') }}°C, Inlet 2: {{ states(''sensor.miner_inlet_temperature_2'')
        }}°C. Please check your miner''s cooling system.'
      target: 14164327184@c.us                                                                                                                                          

Electricity Price Monitoring

Mining is only profitable below a certain electricity cost. Based on AI-assisted calculations, I set my limit to 62c/kWh. If the price rises above this, Home Assistant turns off the miner:

- id: 634d0fe360ec41c3ac1ea0f3473ad603                                
  alias: Turn Off Miner When Electricity Price Is High                
  description: Turns off the miner when electricity price goes above 62c/kWh
  triggers:                                                           
  - entity_id:                                                        
    - sensor.electricity_cost_in_cents_per_kwh                        
    above: 62                                                         
    trigger: numeric_state                                            
  actions:                                                            
  - target:                                                           
      entity_id: switch.shellyplug_s_3ce90ee04b97                     
    action: switch.turn_off                                           
    data: {}                                                          
- id: 9c4564819e404cff95fcc447532bd19b                                
  alias: Turn On Miner When Electricity Price Is Low                  
  description: Turns on the miner when electricity price goes below 62c/kWh                                                                                             
  triggers:                                                           
  - entity_id:                                                        
    - sensor.electricity_cost_in_cents_per_kwh                        
    below: 62                                                         
    trigger: numeric_state                                            
  actions:                                                            
  - target:                                                           
      entity_id: switch.shellyplug_s_3ce90ee04b97                     
    action: switch.turn_on                                            
    data: {}                

Room Temperature Monitoring

Since I also use a heat pump, I monitor indoor temperatures. If the bedroom temperature drops below 20°C, the heat pump turns on:

- id: bedroom_temperature_control                                                                                                                                       
  alias: Bedroom Temperature Control
  description: Control bedroom heating based on temperature between 9 AM and 7 PM
  trigger:
  - platform: numeric_state
    entity_id: sensor.temperature_bedroom
    below: 20 
  - platform: numeric_state
    entity_id: sensor.temperature_bedroom
    above: 20 
  - platform: time_pattern
    minutes: /15
  condition:
  - condition: time
    after: 09:00:00
    before: '19:00:00'
  action:
  - choose:
    - conditions:
      - condition: numeric_state
        entity_id: sensor.temperature_bedroom
        below: 20
      sequence:
      - service: climate.set_hvac_mode
        target:
          entity_id: climate.ac_12488762
        data:
          hvac_mode: heat
      - service: climate.set_temperature
        target:
          entity_id: climate.ac_12488762
        data:
          temperature: 20
    - conditions:
      - condition: numeric_state
        entity_id: sensor.temperature_bedroom
        above: 21
      sequence:
      - service: climate.turn_off
        target:
          entity_id: climate.ac_12488762
  mode: single

Rewards and Taxation

Initially, I set my mining rewards to be paid in BTC. However, I now distribute them as 1/3 BTC, 1/3 DOGE, and 1/3 LTC, balancing risk and market fluctuations.

For tax reporting, I direct rewards to Binance to simplify tracking. Home Assistant helps by exporting electricity costs directly from its database, making expense calculations easier.

I want to see plenty of things with one look from my mobile Home Assistant Companion App. Expected Rewards, current expenses, temperatures, unpaid rewards in BTC, DOGE, LTC, USD and EUR. I want to see current electricity price with everything including transfer tariff and taxes. Some of the things I want see in graphs, some in numbers. The one thing I want to add is uptime, but other than that I’m cool with my current setup.

As you can see, the current expected profit for whole day is only 0.60€. This does not take into consideration that I also heating the whole house with the miner. As you can also see, the current electricity SPOT price is way higher than average. Same time DOGE is valued significantly lower than the average which lowers the expected rewards.

Reward payments and taxation

When I initially setup the miner and payments I decided to have them on de-centralized wallet. But then the conversion fees there are way higher than on Binance for example. I decided to to get the rewards paid directly to Binance for that reason.

Maybe later when I’m doing millions with this I might consider de-centralized wallet, but at the moment when it seems the miner is not going to be paying itself within next two years if ever I don’t feel pressure to get the little I earn from this to anywhere outside Binance.

I feel also that it is easiest to do tax return when everything I earn is on Binance. Home assistant certainly helps in regards calculating the expenses since I can export monthly electricity costs directly from Home Assistant database tables. I’ve already created few views there to make that part of the mining easier.

Final Thoughts

This setup provides an efficient and automated mining operation while reducing downtime. Let me know if you have suggestions for improvement or spot any flaws in my approach!

Elphapex DG Home 1 Monitoring Update

Profitability

This is a quick update on my mining experience over the past couple of weeks. As mentioned before, I’ve found PowerPool.io to be the most efficient option. However, rewards have dropped significantly recently, likely due to rising mining difficulty and declining DOGE and LTC prices. Both factors have negatively impacted profitability.

In my case, these changes alone have reduced profitability by about 25%–35%. Previously, I could earn up to €0.20 per hour at best, but now it’s closer to €0.15.

Monitoring

Several other factors also affect profitability, one of which is monitoring. Over the last two weeks, my miner has gone offline multiple times. I can spot this by keeping an eye on the hashrate for my worker on PowerPool.io and then automate a reboot, but that doesn’t really help me understand the root cause.

Initially, I suspected a firmware issue—maybe when it loses the primary pool, it fails to properly connect to the next one in line. However, whenever the miner goes down, it also becomes unreachable via its web interface, indicating something else might be wrong. Since the DG Home1 miner doesn’t provide persistent logs, it’s impossible to diagnose the issue after the fact.

There’s also no straightforward way to monitor the miner remotely, short of writing a custom script. To address this, I created a script called miner_monitor.py that generates a simple log and publishes certain parameters to an MQTT Broker:

Through my mqtt.yaml, I then create sensor templates and can keep track of these metrics in real time.

Temperatures

One of the most important metrics to watch is temperature. I suspect that if cooling fails, the miner may overheat. According to the documentation, the upper limit is 85°C, at which point the miner shuts down automatically. I’ve set up a system that sends me both email and WhatsApp alerts if any temperature sensor exceeds a specified threshold.

These parameters are similar to the ones shown in the table abowe, plus two additional temperature readings not available through the web interface shown below.

Notifications

With this setup, my miner not only reboots automatically when hashrate drops, but it also sends notifications explaining why it went offline in the first place.

Conclusion

I hope this simple setup helps others as well. My configuration is somewhat messy, but if you develop an easier or more elegant monitoring system, please let me know. As always, my entire configuration is available on my GitHub repository.

Why mine anything

I’ve been running a DOGE miner for about a week and a half now. I never really thought DOGE was going to be “the next big thing.” I see a bit more potential in Litecoin, but I’m mostly focused on Bitcoin. However, my miner currently doesn’t mine BTC. The BTC miners earns about one-tenth of the profit compared to mining LTC or DOGE. So I rather mine where the money is and put everything to BTC. To be clear: I mine altcoins and take the profit in bitcoin.

To be honest, I’m skeptical about the future of altcoins—especially meme coins. So why am I mining, given that I don’t see much sense in home BTC mining, and I’m not really sold on other cryptocurrencies either? The main reason is that I want to understand the technology. I’ve decided to only invest in BTC, but I still want to explore how other coins work and whether mining Bitcoin at home can ever be truly profitable.

Lately, I’ve become more interested in decentralized AI. The idea is that many of us—myself, you, and others—have GPUs at home. Instead of using them for crypto mining, we could put them to work on decentralized AI projects. Yes, it still consumes electricity, but at least it could serve a more meaningful purpose. We could even apply the same incentive structure that exists in blockchain to reward participants.

Rather than sending our money to large operations in China or the U.S., we could invest in our own local communities by redirecting home mining capacity to AI development. Let’s break free from the current systems!

I know I’m not the only one thinking this. Let’s collaborate and make it happen!

Decentralized AI – dAI.

Integrating Elphapex DG Home 1 crypto miner with Home Assistant

How to Set Up Litecoin Mining with LitecoinPool and Home Assistant

In this guide, we will walk through setting up a Elphapex DG Home 1 miner on LitecoinPool and integrating it with Home Assistant. You’ll learn how to monitor your miner, track profitability, and automate energy management, making your mining experience efficient and cost-effective.

Above the robovacuum in front showing how small the miner actually is. I decided to move the miner later under the stairs for spreading the heat across the apartment more evenly. The noise level is acceptable – around 50db – and comparable what my heat pump generates. I could live without it but I can also tolerate it. It is noticeable but not disturbingly noticeable. That’s what printing money sounds I guess.

Why Mine LTC and DOGE with LitecoinPool?

  • No fees: LitecoinPool offers fee-free mining, maximizing your earnings.
  • Pay-per-Share (PPS): Guaranteed payout per share submitted.
  • Secure payouts: You can receive rewards directly to your wallet.

Step 1: Setting Up Your Miner on LitecoinPool

  1. Create an account on LitecoinPool.
  2. Log in to your account and navigate to the “Settings” section to retrieve your worker credentials.
  3. Configure your miner with the following pool details:
    • URL: stratum+tcp://litecoinpool.org:3333
    • Username: Your LitecoinPool username
    • Password: Your chosen worker password
  4. Ensure your miner is online and submitting shares to the pool.

In the end I ended up setting litecoinpool.org thrice on the miner configuration as seen below. The litecoinpool.org resolves to several IPs so there’s high availability through round robin effect on DNS. The default litecoinpool.org is located in Europe AFAIK and I could have used also us.litecoinpool.org as well us2.litecoinpool.org or even other mining pools, but after tinkering with the idea I figured out this is the most reasonable setup. Initially I had only one pool, and when that thread died, I wasn’ä’t mining anything either. I have covered that issue also on my Home Assistant configuration later on.

When running the miner the dashboard looks like below on mine setup:

The miner itself is easy to setup so I’m not going much to details here. The default user/password is root/root and I got the IP from my router. It had an issue when using both wireless and wired and I ended up not being able to disable wireless due to that, but I’m ok with it, since I only want to have wireless in the end, although the issue indicates how unfinished the current firmware is.

If you wonder the https URL I’m using above it is because I’ve setup accessing the miner through nginx and I have my own DNS as well. The box is not accessible outside my home network without VPN though.

The box does have ssh daemon listening, but I’m not able to log into it with using root / [web password] which is a bit of a shame. Hopefully that will change with future firmwares.

Step 2: Integrating Your Miner with Home Assistant

Home Assistant provides advanced monitoring and automation capabilities for your mining setup. Here’s how to integrate it:

Requirements

  • A running instance of Home Assistant
  • The REST API URL and API key from LitecoinPool
  • A smart plug for controlling your miner (e.g., Shelly Plug)

Adding LitecoinPool API to Home Assistant

First, add the REST API integration to Home Assistant:

- platform: rest
  name: Litecoinpool Market Data
  resource: https://www.litecoinpool.org/api?api_key=YOUR_API_KEY
  method: GET
  scan_interval: 300
  value_template: "OK"
  json_attributes_path: "$.market"
  json_attributes:
    - ltc_eur
    - doge_eur

Creating Sensors for Monitoring

Use these template sensors to track your rewards:

    total_rewards_eur:                                                                                                               
      friendly_name: "Total Rewards EUR"
      unit_of_measurement: "€" 
      value_template: >
        {% set total_rewards_ltc = state_attr('sensor.litecoinpool_market_data', 'user')['total_rewards'] | float(0) %}
        {% set total_rewards_doge = state_attr('sensor.litecoinpool_market_data', 'user')['total_rewards_doge'] | float(0) %}
        {% set ltc_price_eur = state_attr('sensor.litecoinpool_market_data', 'market')['ltc_eur'] | float(0) %}
        {% set doge_price_eur = state_attr('sensor.litecoinpool_market_data', 'market')['doge_eur'] | float(0) %}
        {{ ((total_rewards_ltc * ltc_price_eur) + (total_rewards_doge * doge_price_eur)) | round(2) }}

I have also created sensor template for my SPOT electricity price both in €/kWh and c/kWh:

- platform: template
  sensors:
    electricity_cost_per_kwh:
      friendly_name: "Electricity Cost Per kWh"
      unit_of_measurement: "€/kWh"
      value_template: >
        {% set spot_price = (states('sensor.nordpool_kwh_fi_eur_3_10_0255') | float(0)) / 100 %}
        {% set electricity_tax = 0.0283 %}
        {% set transfer_day = 0.0262 %}
        {% set transfer_night = 0.0137 %}
        {% set now = now() %}
        {% set transfer_cost = transfer_night if now.hour >= 22 or now.hour < 7 else transfer_day %}
        {{ (spot_price + electricity_tax + transfer_cost) | round(4) }}
    electricity_cost_in_cents_per_kwh:
      friendly_name: "Electricity Cost in Cents Per kWh"
      unit_of_measurement: "c/kWh"
      value_template: >
        {% set spot_price = (states('sensor.nordpool_kwh_fi_eur_3_10_0255') | float(0)) %}
        {% set electricity_tax = 2.83 %}
        {% set transfer_day = 2.62 %}
        {% set transfer_night = 1.37 %}
        {% set now = now() %}
        {% set transfer_cost = transfer_night if now.hour >= 22 or now.hour < 7 else transfer_day %}
        {{ (spot_price + electricity_tax + transfer_cost) | round(4) }}

When above are set I get nice graph for my hash rate and real electricity price including taxes and transfer fees:

Plus I get daily rewards, paid rewards, profitability, electricity consumption and all key parameters to monitor the appliance.

Automating Miner Power Management and Crash Detection

I created an automation to turn off the miner if electricity prices exceed a threshold when it is not profitable anymore. When electricity price goes lower the miner will be powered on again. I might need to change the threshold, but below is based on current profitability calculation without taking profit generated by heating the house:

 alias: Turn Off Miner When Electricity Price Is High
  description: "Turns off the miner when electricity price goes above 33c/kWh"
  trigger:
    - platform: numeric_state
      entity_id: sensor.electricity_cost_in_cents_per_kwh
      above: 33
  action:
    - service: switch.turn_off
      target:
        entity_id: switch.shellyplug_s_3ce90ee04b97
 
- alias: Turn On Miner When Electricity Price Is Low 
  description: "Turns on the miner when electricity price goes below 33c/kWh"
  trigger:
    - platform: numeric_state
      entity_id: sensor.electricity_cost_in_cents_per_kwh
      below: 33
  action:
    - service: switch.turn_on
      target:
        entity_id: switch.shellyplug_s_3ce90ee04b97

I also wanted to restart the miner if there is no mining detected on Litecoinpool. For this I use hashrate. If hashrate is 0 then I will reboot the miner.

- alias: Reboot or Power Cycle Miner
  description: Reboot the miner on low hash rate, fallback to power cycle if needed
  trigger:
    - platform: state
      entity_id: sensor.litecoinpool_hashrate
      to: "0"
      for: "00:15:00"
  condition: []
  action:
    - service: button.press
      target:
        entity_id: button.shellyplug_s_3ce90ee04b97_reboot
    - delay: "00:10:00"  # Wait 10 minutes to see if reboot resolves the issue
    - condition: state
      entity_id: sensor.litecoinpool_hashrate
      state: "0"  # Hashrate is still 0
    - service: switch.turn_off
      target:
        entity_id: switch.shellyplug_s_3ce90ee04b97
    - delay: "00:00:10"  # Wait 10 seconds
    - service: switch.turn_on
      target:
        entity_id: switch.shellyplug_s_3ce90ee04b97
  mode: single

Benefits of Integrating Mining with Home Assistant

  • Real-time monitoring: Track hash rate, rewards, and energy consumption.
  • Profitability tracking: Automatically calculate earnings and costs.
  • Energy efficiency: Automate miner power based on electricity prices.

Profitability

This is small miner so it will not generate much profit, but at best it is around 4€/day. It also generates a lot of heat which saves me on heating costs. That being said, I have heat pump which is more effective on heating. On the other hand I save on heating more than just the direct cost, since I can deduct the electricity cost from tax return on profit

We will see where the average profit will land – that depends on DOGE price as well as on how the mining rewards will progress in the future. I’d expect the miner not to be gold mine, but I also assume it will generate some profit and capital gain within next five years. It is more of a hobby than real money maker though.

Initial conclusion

By combining LitecoinPool and Home Assistant, you can optimize your mining setup for maximum profitability and efficiency. The integration allows you to monitor rewards, manage costs, and automate operations seamlessly.

Choosing the mining pool

I initially chose LitecoinPool because it has good profitability and easy to setup. LitecoinPool is excellent choice in my opinion but obviously no one should not settle with the first choice. I investigated a bit more. It seems at least if you check from powerpool.io that PowePool has the highest rewards and litecoinpool.org comes 2.4% with less rewards when I checked. Obviously PowerPool is biased on this, so I need to monitor longer to do the final decision.

Anyhow since powerpool.io lets choose the crypto currency to get the rewards paid on I decided to give it a try. In my case just getting everything in BTC makes sense, since I have actually planned to convert all the rewards to BTC anyway. I haven’t really decided what my final strategy will be though.

LitecoinPool.org

Within my first week of experimenting I started with litecoinpool.org where I ended up earning around 25€ for the few days there. I liked the API options available and how the web page is simple. The reward are payed on both DOGE and LTC and payments are easy to set up. When you set for example payments to 30DOGE and 0,001LTC you get paid almost every second day around 11€.

Powerpool.io

Powerpool.io is my cohice for now, since it seems to provide be best profitability and I can get paid everything in BTC so I don’t need to do any conversions which in my country are by the way all taxable operations and needs to be reported on tax return.

mining-dutch.nl

I tried out also mining-dutch.nl. It has the widest selection of options and coins you can get your rewards. The profitability on all these three depends on various factors. I didn’t run on mining-dutch.nl to really receive any rewards. BUt as you can see from below picture, it truly supports several coins in a way miner can see exactly what he or she is mining.

Switching to to a different pool means basically just changing the stratum UR, username and password. These

Elphapex configuration for PowerPool

Since I decided in the end stick on PowerPool here are the few things I did there.

Setting up another pool is as easy as create an account and the new miner URLs on Elphapex miner page and save. Even restart is not necessarily needed.

Above pictures illustrates powerpool.io and rewards in about 48 hours after I changed the configuration.

PowerPool HomeAssistant Dashboard

Setting up the Home Asisstant Dashboard with same setup as I have for LitecoinPool is a bit harder, since PowerPool API is missing certain reward data – expected daily rewards for example – which makes it much harder to create similar profitability values as I have setup for LitecoinPool. I have currently following confirguration for PowerPool on my sensors.yaml:

- platform: rest         
  name: "PowerPool Hashrate"                                                                                                        
  resource: !secret powerpool_user_api_url
  value_template: "{{ value_json.data.hashrate.scrypt.hashrate | float }}"
  unit_of_measurement: "GH/s"
  scan_interval: 300

- platform: rest         
  name: "PowerPool Rejection Rate"                                                                                                        
  resource: !secret powerpool_user_api_url
  value_template: "{{ value_json.data.workers[0].rejectedPercentage | float }}"
  unit_of_measurement: "%"
  scan_interval: 300

- platform: rest         
  name: "PowerPool BTC Balance"                                                                                                        
  resource: !secret powerpool_user_api_url
  value_template: >
    {% for balance in value_json.data.balances %}
      {% if balance.coinTicker == 'BTC' %}
        {{ balance.balance | float }}
        {% break %}
      {% endif %}
    {% endfor %}
  unit_of_measurement: "BTC"
  scan_interval: 300

- platform: rest         
  name: "PowerPool USD Balance"                                                                                                        
  resource: !secret powerpool_user_api_url
  value_template: >
    {% for balance in value_json.data.balances %}
      {% if balance.coinTicker == 'BTC' %}
        {{ balance.balanceUSD | float }}
        {% break %}
      {% endif %}
    {% endfor %}
  unit_of_measurement: "USD"
  scan_interval: 300

- platform: rest         
  name: "PowerPool BTC Rewards"                                                                                                        
  resource: !secret powerpool_user_api_url
  value_template: >
    {% for reward in value_json.data.workers[0].totalRewards %}
      {% if reward.coinTicker == 'BTC' %}
        {{ reward.totalRewards | float }}
        {% break %}
      {% endif %}
    {% endfor %}
  unit_of_measurement: "BTC"
  scan_interval: 300

I created following card yaml:

type: vertical-stack
cards:
  - type: history-graph
    entities:
      - entity: sensor.powerpool_hashrate
    hours_to_show: 24
    refresh_interval: 60
    name: Hashrate (Last 24h)
  - type: entities
    title: PowerPool Metrics
    entities:
      - entity: sensor.powerpool_hashrate
      - entity: sensor.powerpool_btc_balance
      - entity: sensor.powerpool_usd_balance
      - entity: sensor.powerpool_rejection_rate
  - type: entities
    title: Miner Energy Costs
    entities:
      - entity: sensor.electricity_cost_in_cents_per_kwh
        name: Electricity price
        icon: crypto:fiat-eur
      - entity: sensor.shellyplug_s_3ce90ee04b97_energy_cost_hourly
        name: Cost per Hour
        icon: crypto:fiat-eur
      - entity: sensor.shellyplug_s_3ce90ee04b97_energy_cost_daily
        name: Cost per Day
        icon: crypto:fiat-eur
      - entity: sensor.shellyplug_s_3ce90ee04b97_energy_cost_weekly
        name: Cost per Week
        icon: crypto:fiat-eur

Above card creates something like like below with running about 10 hours:

I spent a whole day figuring out how to get same configuration I had with Litecoinpool done within couple of hours. I ended up getting much more information in the end. The below card has pretty much all I could think of.

The full configuration

The code snippets above does not have my full Home Assistant configuration, but as normal you can check the whole configuration from my GitHub Repo.

Migrating from Gallery Menalto 1.x to Piwigo: An Open Source Solution

Introduction

Migrating a large collection of photos and albums from an outdated photo gallery software can be a daunting task. With the aim of addressing this challenge, I have developed a robust framework to facilitate the migration from Gallery Menalto to Piwigo. This open-source project, licensed under GPL 3, not only aids in migrating to Piwigo but also provides the flexibility to migrate to any photo gallery software with an open API.

Background

Around 2004, I deployed a self-hosted photo gallery to organize, store, and maintain digital photos. At the time, this was a new area for me, and I had little experience managing digital images. The motivation was clear: my first daughter had just been born, and I wanted to ensure that these precious memories were easily accessible not only for myself but also for other family members, including grandparents.

For various reasons, I chose Gallery Menalto as my gallery software and ran it on OpenBSD 4.0 with a Geeklog 1.3 website. This setup was hosted in my closet in an apartment in Finland. Ten years later, several more daughters had been born, and I had moved to Canada. Despite the passage of time, both GeekLog and Gallery Menalto were still running on the same old OpenBSD system, which I had virtualized on top of VMware Server 2.0. The whole system had moved from the closet to the basement.

By then, I had amassed over 10,000 photos, and I was increasingly stressed about how to migrate them from the legacy system. Although I had attempted several times to upgrade from OpenBSD to Linux, the process was far from simple.

Another ten years passed, and the system was back in Finland, still running on a virtualized OpenBSD on top of VMPlayer. I couln’t even get the VM to run on libvirt because the OpenBSD 4.0 can’t handle that. Upgrading had become even more challenging; what was a legacy system ten years ago had become practically prehistoric.

I searched extensively for a solution, but nothing seemed to work. The Gallery version was 1.4, which couldn’t be upgraded to 2.x and then to 3.x, making the transition to something like Piwigo impossible. OpenBSD hadn’t received any updates either, but the environment was jail-rooted, and Gallery 1.x, with its known internals, didn’t pose significant security risks – it was wide open anyway.

Since no ready-made solution was available, I decided to create my own. I started by migrating all the articles from Geeklog to WordPress, a task that took about two nights and was relatively straightforward – if you can call something where you need to have gateway MySQL to run on docker container because of the legacy source system and bunch of python scripts straight forward, it was straightforward. Next, I delved into the internals of Gallery 1.4, experimenting through trial and error. Within a few nights, I had a plan.

Instead of attempting to migrate everything at once, I decided to collect the metadata from Gallery 1.x in one phase and perform the actual migration in a second, separate phase. This approach meant that the first phase was not dependent on the second, allowing me to choose any target gallery software if I decided against Piwigo later on.

Once this idea took shape, I managed to get everything working relatively quickly. I started the migration yesterday, and as of now, I have successfully completed it. I had to make a few changes to the code along the way, but the final version is now available in my GitHub repository.

Project Overview

The project comprises two main Python scripts:

  1. Collect Gallery Metadata (collect_gallery_meta_data.py): This script gathers meta information for all albums and photos from Gallery Menalto.
  2. Execute Migration (execute_migration.py): This script performs the actual migration of albums and photos, along with their metadata, to Piwigo.

Collecting Gallery Metadata

The collect_gallery_meta_data.py script is designed to traverse through the albums and photos in a Gallery Menalto installation, collecting crucial metadata and storing it in a MySQL database (or any SQLAlchemy-supported database). This metadata includes:

  • Album name
  • Parent album
  • Album caption
  • Album title
  • Album description
  • Photo filename
  • Photo caption
  • Photo title

How It Works

  1. Command-line Argument: The script takes an album located in the Gallery 1.x root as a command-line argument.
  2. Traversal: It recursively goes through all sub-albums and photos, collecting metadata.
  3. Storage: The collected data is stored in database tables (albums and photos)

Executing the Migration

The execute_migration.py script utilizes the metadata collected to perform the migration to Piwigo. It:

  • Creates the root album and sub-albums in Piwigo.
  • Uploads photos along with their metadata (capture date, upload date, caption, title, description) to Piwigo.

Key Features

  1. Download Photos: Photos are downloaded from Gallery Menalto only once. The field downloaded is set to 1 when the download is complete.
  2. Upload Photos: Once a photo is successfully uploaded to Piwigo, the field uploaded is set to 1.
  3. Track Migration: The albums table has a column migrated, which is set to 1 once an album is migrated. This ensures the process can be safely resumed if interrupted.

Advantages and Applications

This framework is highly beneficial for users who wish to transition from Gallery Menalto to modern photo gallery software. Its primary advantages include:

  1. Flexibility: While designed for Piwigo, the framework can be adapted for any photo gallery software with an open API.
  2. Efficiency: Metadata and photos are efficiently handled and migrated, ensuring data integrity and completeness.
  3. Open Source: As an open-source project under the GPL 3 license, it encourages collaboration and customization.

Getting Started

Clone the Repository:

git clone https://github.com/elsonico/gallery-piwigo-migration.git
cd gallery-piwigo-migration

Set Up the Database:

Ensure you have a MySQL database (or any SQLAlchemy-supported database) ready. Here’s the DDL for the two tables:

albums

CREATE TABLE `albums` (
  `id` int NOT NULL AUTO_INCREMENT,
  `name` varchar(255) NOT NULL,
  `parent_id` int DEFAULT NULL,
  `metadata` text,
  `meta` text,
  `title` varchar(255) DEFAULT NULL,
  `caption` text,
  `description` text,
  `migrated` tinyint(1) DEFAULT '0',
  `created` tinyint(1) DEFAULT '0',
  PRIMARY KEY (`id`),
  KEY `parent_id` (`parent_id`),
  CONSTRAINT `albums_ibfk_1` FOREIGN KEY (`parent_id`) REFERENCES `albums` (`id`)
)

photos

CREATE TABLE `photos` (
  `id` int NOT NULL AUTO_INCREMENT,
  `album_id` int DEFAULT NULL,
  `filename` varchar(255) NOT NULL,
  `caption` text,
  `metadata` text,
  `meta` text,
  `type` varchar(10) DEFAULT NULL,
  `description` text,
  `url` varchar(255) DEFAULT NULL,
  `downloaded` tinyint(1) DEFAULT '0',
  `uploaded` tinyint(1) DEFAULT '0',
  `capturedate` datetime DEFAULT NULL,
  `uploaddate` datetime DEFAULT NULL,
  PRIMARY KEY (`id`),
  KEY `album_id` (`album_id`),
  CONSTRAINT `photos_ibfk_1` FOREIGN KEY (`album_id`) REFERENCES `albums` (`id`)
)

Collect Metadata

Before starting the metadata collection you need to have few environment parameters set, since the code expects to have them:

  • GALLERY_BASE_URL
    • This is your gallery URL including your album location: https://[gallery_host]/[albums]
    • This is the location you can find your albums.dat
  • DATABASE_URL
    • This is your MySQL URL: mysql+pymysql://[user]:[password@[host]/[database]

Once all above including the database tables are created your are good to go:

python collect_gallery_meta_data.py [root_album]

Execute Migration

As for collecting the metadata for doing the migration you need several environment variables set:

  • GALLERY_BASE_URL
    • Same as above depending on your configuration: https://[gallery_host]/[albums]
  • PIWIGO_API_URL
    • This is: https://[piwigo_host]/piwigo/ws.php
  • PIWIGO_USERNAME / PIWIGO_PASSWORD
    • Piwigo credentials with privileges to create albums and add photos
  • MIG_DB_HOST / MIG_DB_NAME
    • The database host and database your store the albums and database tables
  • MIG_DB_USER / MIG_DB_PASSWORD
    • Credentials for the migration database
  • PW_DB_HOST / PW_DB_USER / PW_DB_PASSWORD / PW_DB_NAME
    • The Piwigo database host, database and credentials

Once you are certain above environmental variables are correctly set you are good to go and migrate the Gallery albums from root album level album by album to Piwigo.

The migration start with below command:

python execute_migration.py [root_album]

The program expects to find album information collected for [root_album] so you must ensure you have data collected with collect_gallery_meta_data.py before getting to this phase. Note also, that you can edit the meta information through database tables if you want to change anything there.

Conclusion

Migrating from Gallery Menalto to Piwigo, or any other modern photo gallery software, is now more manageable with this open-source framework. By leveraging the power of Python and SQLAlchemy, this project ensures a seamless transition, preserving all your precious photo memories. I warmly welcome contributions and collaboration to further enhance and extend this tool.

Book review: Girl in a band

Girl in a BandGirl in a Band by Kim Gordon
My rating: 3 of 5 stars

In my opinion, there are a few strange and misogynistic reviews, so I’d like to offer my perspective.

This is an autobiography, not just a book about Sonic Youth. It is a chronicle of Kim Gordon’s life up to 2014, covering her childhood, adolescence, and adulthood. Naturally, it includes aspects of her life outside of music.

Regarding the criticism of “name dropping” mentioned in some reviews: people in the music industry tend to have friends within the same industry. Should these names be anonymized to avoid “name dropping”?

The book itself is well-written, mostly in chronological order. It provides many details about Kim’s personal life, including her marriage. She speaks fondly of Thurston, clearly admiring his talent and his role as a father, as well as his contributions in other areas of life. They were a fantastic couple and achieved a lot together.

As mentioned, Sonic Youth is not the sole focus of the book; it isn’t even the primary subject. Kim as an artist is.

Based on the book, Kim comes across as modest and honest. She doesn’t seem bitter about anything and accepts things as they are.

I wish Kim all the best in her future endeavors in life and art.

It was a good and interesting read.

View all my reviews

Introducing the Latest Features of OpenAI GPT-4o: Transforming AI Interactions

Here’s some thrilling updates on the latest iteration from OpenAI: GPT-4o. As we dive deeper into this rapidly-evolving digital landscape, GPT-4o promises to be a game-changer, offering new functionalities and advancements that are set to transform our interactions with AI. Let’s break down these innovations and explore what makes GPT-4o stand out.

Enhanced Contextual Understanding

Context Preservation:

GPT-4o comes equipped with advanced contextual memory, allowing it to maintain more extended and nuanced conversations. The model can remember previous interactions and references, creating a more seamless and human-like interaction. For instance, if you discussed your favorite book two weeks ago, GPT-4o can refer back to that conversation with remarkable accuracy.

Multimodal Capabilities

Text, Image, and Beyond:

One of the most anticipated features is its multimodal capability, which means GPT-4o can process and generate not just text but also images. This opens a plethora of possibilities for applications in e-commerce, digital marketing, content creation, and more. Imagine describing a concept and having GPT-4o generate a detailed image or selecting an image and receiving an elaborate textual description – all with unprecedented fluency!

Real-time Adaptability

Learning on the Fly:

With the real-time adaptability feature, GPT-4o can now learn and adapt from interactions on the go. This allows for a more personalized user experience, as the model fine-tunes its responses based on continuous feedback. This ability to adapt dynamically ensures that each interaction is tailored and optimized for the user.

Enhanced Security and Ethical Considerations

Bias Mitigation and Content Filtering:

In light of growing concerns about AI ethics, GPT-4o places a strong emphasis on bias mitigation and responsible AI usage. OpenAI has implemented rigorous content filtering and bias detection mechanisms to ensure safer and more equitable interactions. The model has been trained to identify potential biases and strives to offer balanced and fair responses.

Developer-friendly Integrations

API Upgrades:

For the developer community, GPT-4o brings a suite of powerful tools and upgrades to its API, making integration smoother and more efficient. Enhanced documentation, more comprehensive support libraries, and example use cases empower developers to leverage GPT-4o’s capabilities swiftly and effectively, fostering innovation across various sectors.

Advanced Emotional Intelligence

Sentiment Analysis and Response:

GPT-4o’s emotional intelligence has seen significant enhancement, enabling it to perform more sophisticated sentiment analysis. Understanding and appropriately responding to a wider range of human emotions – from excitement to concern – makes interactions more meaningful and empathetic, bridging the gap between human and machine communication.

Augmented Creativity Tools

Creative Writing and Beyond:

Whether you’re crafting poetry, drafting an email, or brainstorming ideas, GPT-4o’s augmented creativity tools offer unparalleled assistance. The model can now generate creative content that aligns more closely with specific tones, themes, and styles, providing users with a versatile tool for content creation and ideation.

Language Translation and Localization

Multilingual Mastery:

GPT-4o expands its language capabilities with improved translation and localization features. It can now translate text with greater accuracy and nuance, supporting more languages and dialects than ever before. This paves the way for more inclusive and accessible communication across global audiences.

Conclusion

GPT-4o represents a significant leap forward in the field of artificial intelligence, integrating more advanced features and capabilities that enhance user experience, boost productivity, and prioritize ethical considerations. Whether you are a developer, a business owner, or simply an AI enthusiast, GPT-4o offers a myriad of possibilities to explore and exploit.

I am excited to see the myriad ways in which GPT-4o will be applied across different industries and workflows. Stay tuned to auroranrunner for more updates and deep dives into the latest in tech innovations!

Feel free to share your thoughts, experiences, and predictions about GPT-4o in the comments below.

Kurzweil’s Age of Spiritual Machines: A Contemplation of Future Reality

Kurzweil’s Age of Spiritual Machines: A Contemplation of Future Reality

Today, we delve into an intriguing piece of literature from legendarily futuristic thinker, Ray Kurzweil – “The Age of Spiritual Machines: When Computers Exceed Human Intelligence.”

A Published Marvel from the Past for the Future

First, let’s rewind time back to 1999. At a time when deep fears of Y2K Computer Bug-induced apocalypse were profound, Ray Kurzweil forged a path to the future with his audacious predictions in “The Age of Spiritual Machines”. In this compelling piece, Kurzweil espouses technological singularity, where artificial intelligence (AI) surpasses human understanding and capabilities.

AI: A New Breed of Intelligence

According to Kurzweil, a new breed of intelligence projects our evolution. Think about intelligent machines, the kind that not only perform complex tasks with unparalleled precision but also exhibit consciousness. An age where computers would encapsulate spirituality previously attributed exclusively to humans.

Kurzweil demystifies this concept by detailing a timeframe leading to the ‘singularity’. He argues that technology’s exponential growth will eventually result in machines becoming as intelligent as humans by the 2020s, developing their own emotions and consciousness by the mid-21st century.

The Intersection of Technology and Human Life

One such intersection between technology and human life is the concept of uploading human consciousness into machines. Kurzweil mulls the merging of biological and artificial intelligence, culminating in a future where humans will have the ability to ‘download’ their mentalities into non-biological entities, thus attaining virtual immortality. This ushers in an era where one’s existence continues perpetually, with their consciousness meandering in the virtual realm.

Challenges and Debates

Undoubtedly, Kurzweil’s predictions invite controversies. Critics argue that consciousness is not quantifiable or replicable, considering it an exclusively human phenomenon. The concept of spiritual machines necessitates an overhaul of identity, ethics, reality, and even humanity.

Despite the controversy, Kurzweil’s projections have turned out to be remarkably prescient. Today, advancements in computational capabilities, deep learning, neural networks, and AI are gradually realizing the contours of Kurzweil’s vision, even though we’re yet to witness machines acquire human-level intelligence or spirituality.

Final Thoughts

“The Age of Spiritual Machines” is more than a book about machines. It offers a vision of the future, challenges our understanding of consciousness, and stands as a testament to mankind’s unending quest for progress. Whether you find this vision unnerving or thrilling, there’s no denying that Kurzweil’s work garners contemplation.

As we grapple with the ethical, social, and existential dilemmas brought by our technological trajectories, it is key to engage with works like Kurzweil’s, soaking in their wisdom and ideals to navigate this shared futuristic reality.

And remember, we’re living in an era of rapid discovery and advancement – Tomorrow’s literature could very likely become the future’s reality!

Migrating Your Home Assistant Database from SQLite to PostgreSQL

Migrating your Home Assistant database from SQLite to PostgreSQL can significantly enhance performance, especially as your data grows. This guide will walk you through creating a database dump, converting data types, setting up your PostgreSQL database, and configuring Home Assistant to use the new database.

Creating the Database Dump from SQLite

To start, you need to create a dump of your existing SQLite database:

sqlite3 home-assistant_v2.db .dump > ha_dump.sql

This command creates a plain text file containing the SQL commands needed to reconstruct the database.

Converting Data Types

During the migration from SQLite to PostgreSQL, certain data types need to be converted:

  • DATETIME to TIMESTAMP: PostgreSQL uses TIMESTAMP instead of DATETIME.
sed -i 's/DATETIME/TIMESTAMP/g' ha_dump.sql
  • BLOB to BYTEA: Convert BLOB fields to BYTEA for binary data.
sed -i 's/BLOB/BYTEA/g' ha_dump.sql

Preparing PostgreSQL Database

Creating Database and User

Start by setting up your PostgreSQL database and user:

CREATE DATABASE homeassistant;
CREATE USER ha WITH ENCRYPTED PASSWORD 'yourpassword';
GRANT ALL PRIVILEGES ON DATABASE homeassistant TO ha;

Create the objects and load the data

Use the command line utility psql to create the database objects and load the data into the newly created PostgreSQL database:

psql -h [your_db_host]-U ha -d homeassitant -f ha_dump.sql -W > load.log 2>&1

Instructions:

  • Replace [your_db_host] with the actual hostname or IP address where your PostgreSQL database is hosted.
  • User and Database: Ensure that ha is the correct username and homeassistant is the correct database name you created for Home Assistant.

After running the command, the SQL dump file (ha_dump.sql) will be executed against your PostgreSQL database. The output and any errors encountered during the process will be redirected to load.log. This log file is essential for tracking the progress and identifying any issues that need resolution.

Check for Errors:

  • Review the load.log file for possible errors. This file contains all output from the psql command, including any SQL errors or warnings that were generated during the import process.
  • Iterate as Necessary: If errors are found, you may need to fix issues in ha_dump.sql and rerun the command. This step might need to be repeated several times. Modifications could involve correcting data types, adding missing sequences for auto-incrementing fields, or adjusting SQL syntax to be compatible with PostgreSQL.

This process can be time-consuming, but it is crucial for ensuring that your database is correctly set up with all the necessary data and schema configurations. As noted, your mileage may vary depending on the specifics of your data and the initial state of the ha_dump.sql file.

Adjusting the Schema

For tables needing auto-increment functionality (which is common in primary key columns), set up sequences:

CREATE SEQUENCE states_state_id_seq;
ALTER TABLE states ALTER COLUMN state_id SET DEFAULT nextval('states_state_id_seq');
SELECT setval('states_state_id_seq', (SELECT MAX(state_id) FROM states) + 1);

Repeat this pattern for other necessary tables and columns, such as events(event_id), state_attributes(attributes_id), and so on.

Configuring Home Assistant

Install SQLAlchemy and other dependencies

Home Assistant uses SQLAlchemy as the SQL toolkit and Object-Relational Mapping(ORM)system for Python. Install it with pip:

pip3 install SQLAlchemy<br>pip3 install psycopg2-binary

Modify the configuration

Modify the configuration.yaml file to point to the new PostgreSQL database:

recorder:
  db_url: postgresql://ha:yourpassword@localhost/homeassistant

This setup directs Home Assistant to use the newly configured PostgreSQL database.

Benefits of Migrating to PostgreSQL

Moving from SQLite to PostgreSQL offers several benefits:

  • Scalability: PostgreSQL handles larger databases and more concurrent connections.
  • Performance: Improved query performance and optimization options.
  • Reliability: Robust transaction support and recovery features.
  • Flexibility: Richer set of data types and full-text searching capabilities.

Conclusion

Migrating your Home Assistant database to PostgreSQL not only enhances performance but also provides a more robust and scalable backend, suitable for growing smart home environments. This migration ensures that your Home Assistant setup can handle increased data loads efficiently and reliably.

Famous last words

One of my primary concerns during this migration was the potential loss of historical data, particularly how it might affect critical metrics like energy usage. The statistics table, which was the last to have the auto-incremental column added, is pivotal as it houses the energy usage stats.

As the image below shows, there appears to be a gap of approximately four hours in the data on energy usage stats. However, it seems that Home Assistant has effectively compensated for this missing data. The system appears to have aggregated the missing energy usage from those four hours into the data represented in the 5 PM bar on the chart.

This outcome is quite reassuring and confirms that the system’s integrity remains intact despite the migration hiccups. I’m relieved to see that after all the adjustments and troubleshooting, everything is functioning as expected.

This experience underscores the importance of careful planning and execution in database migrations, especially when dealing with essential home automation systems like Home Assistant. The transition may require significant effort and attention to detail, but the end result can be gratifying, ensuring continuity and robustness in data handling.

Building a DIY AI Chatbot: Control Your Conversations

Introduction

A self-built AI chatbot is crafted entirely by an individual or team from scratch, without relying on pre-existing templates or platforms. This approach gives developers complete autonomy over the coding, features, and functionalities of the chatbot.

Creating a self-built AI chatbot demands a blend of programming expertise, a deep understanding of artificial intelligence, and inventive thinking. Developers can use a variety of programming languages, including Python, Java, or JavaScript, based on their preferences and the chatbot’s intended application.

One of the standout advantages of a self-built AI chatbot is its high level of customization. Developers can fine-tune the chatbot’s responses and functionalities to meet specific needs and objectives. Moreover, they can continually refine and enhance the chatbot on their own timetable, independent of external updates or support.

Getting started

Building a chatbot from scratch might seem daunting, but it’s quite feasible with the right tools. I used the OpenAI API and the Python openai library (version 1.23.2 as of this writing). While GPT-4 typically suggests using openai==0.28, the transition to versions above 1.0 signifies substantial changes and necessitates thoughtful consideration. However, this doesn’t mean that ChatGPT cannot assist in coding—it can, though it requires precise instructions.

Technical setup

For my project, the technical foundation included:

  • Python 3.9.x or higher: I chose Flask as the application server.
  • Access to the OpenAI API: Essential for integrating the AI logic into the chatbot

This setup is sufficient to establish a testing environment for the AI logic, connecting the Python code to the OpenAI API.

Advanced configuration

After thorough testing, I moved on to production. I continued using Flask for its simplicity, but also added Gunicorn as a frontend server. The application runs either as a standalone version or embedded within a WordPress blog.

I explored different operational models, including storing interactions in a database and the Bring Your Own Data (BYOD) model, although the latter’s impact on performance is still unclear. Initially, I deployed the gpt-3.5-turbo-instruct model for its speed and contextual retention. However, for superior output quality, I ultimately chose GPT-4 despite its slower response time.

The AI Bot Herself

The embedded ChatBot is utilizing gpt-3.5-turbo-instruct whereas the one on below links is utilizing gpt-4 model. The later needs a bit time to think, but she will get there… You can compare the results.

Conclusions

A self-built AI chatbot can serve myriad purposes—customer support, entertainment, educational assistance, or personal aid, and can be integrated across websites, messaging platforms, or mobile apps.

For me, the project was primarily an exploration of AI technologies and the OpenAI API. It was also an invaluable learning experience in Python, application servers, and container technologies.

Building a self-built AI chatbot is undoubtedly a complex, resource-intensive endeavor that necessitates ongoing updates and maintenance. Yet, the potential for continuous learning and improvement through natural language processing and machine learning algorithms makes it increasingly efficient and precise over time.

From a Friday morning start to a productive Monday evening, my journey with this project underscores the potential and versatility of AI technologies, making a self-built AI chatbot a potent, customizable tool for any tech-driven initiative.

References