Sad news about Hactar Go Lite

Update 2024-07-18: Hactar Go is normally available. Hactar Go Lite is not available to new users, and will never be. Existing users of Hactar Go Lite can use Lite as long as they have it installed.

Update 2024-07-15: Google has reinstated the Hactar. Now Hactar Go can be installed again. For Hactar GO Lite I am not sure yet, I hope it will also be available for new users.


Google has suspended both Hactar go and Hactar go lite as of 2024-07-12. This means that Hactar is not available from Google Play for new users, and I cannot manage the applications in any way.

Google seems not to approve having essentially same application as free and paid. As result BOTH are now suspended as “repetitive content”.

And impact to users? Those who have existing installation of Hactar can continue to use it, but installing to a new device is not possible. All subscriptions will work until next renewal, but subscription renewals will not happen.

Funny thing is this is that Google instructs me “Action required: Publish a new compliant version of your app”. But they have removed my ability to do any changes to the applications!

If Google does not change their mind about the suspension, Hactar will never be available in Google Play. Also it is highly unlikely that I would publish any new go related application in Google play. Perhaps Hactar may re-appear in some other app store, or be available as side-loadable application. Only time will tell.

New Atarigo bot

Do you think you are good at capture go (atarigo)? It is an easy game for beginners, right?

Now even a strong player can test their real skills at atarigo. Hactar go has got renewed server-based atarigo bot, “Capture GO master”.

“Capture GO master” bot has been trained using reinforcement learning, so the same technique the alphazero used. It is quite strong. To be honest I don’t know how strong it is, as I have not won the released version…

A new atarigo bot may not be world-shaking news for most of us. But internally it is a big step in creating new Hactar tools and renewing some of the old infrastructure.

Update to Katago networks

Distributed Katago training ( has produced a new improved network a while ago. The network is known by nice name “b18c384nbt”. I installed the network to Hactar servers a few days ago, and they seem to work fine.

Networs are about 20% faster than old ones used in Hactar server. At the same time networks are stronger that old networks. So Hactar should be now both a bit faster and a bit stronger!

Improvement in the network is due to a new structure of the network. New network is still learning faster that old network was, so I will probably update the network once their learning speed has settled.

Better 19×19 engine

Hactar is finally retiring well-served Pachi engine for 19×19 boards. The Pachi engine cannot meet modern expectations, where AI easily beats humans. The 9×9 and 13×13 bots are still be Pachi-based, they will be converted to new AI engine later.

New engine is combination of katago engine and a custom model for human-level play. This allows the bot to play very human-like game.

The custom model is trained using human games. As most games were bit older, it mostly plays with somewhat older style. The model seems to be quite good at tactics and ladders. So even if it is weaker than katago, it is not easy to trick.

I will be adjusting the engine further in following days.

Let me know how the new engine feels!

Update: The new engine still bit bad with white and high handicaps. So Pachi is still doing the job for high handicaps.

GOWrite facelift

GOWrite has been looking quite dated for many years. This may not impact the way GOWrite can be used, but it definitely makes it less enjoyable.

Now I have integrated new skin to GOWrite, and this makes GOWrite look reasonably modern again!

There are also new icons, also much more modern than the inconsistent set of old icons. As the new icons are purely vector graphics, they also scale properly in different displays.

Below is sample of the new look.

Hactar Go Update

During last 3 months I have pushed out series of improvements to Hactar GO. Main improvements:

  • Now I consider Ai analysis to be of production quality.
  • SGF editor screen layouts are better in almost all screen sizes.
  • Partial localizations have been improved, now most important texts should be there.
  • Board graphics are more readable.
  • Game score is cleaner and more readable.
  • Stability is a lot better now, ANR problems should be solved.

If you are interested in improving a localization or creating a new localization, some instructions are in To minimize typing, I have integrated DeepL and google translation engines. Their proposals can be used as a starting point. And don’t hesitate to contact me, I am happy to help!

For a while there was a bug in Hactar Go Lite which prevented use of Ai. This is now fixed and Ai is quite usable also without any subscription in Lite.

From this I will continue to make minor improvements to Ai and graphics. And in parallel I will continue to work for new, interesting features!

Hactar Go Analysis

Hactar go version 3.0 introduces game analysis tool. Tool makes it easy to analyzing one’s games, or professional games, with any Android phone or tablet. As heavy computation is done in the cloud, the phone or tablet do not need to be high-end model for fast analysis.

Now the tool is available in most recent version of both Hactar Go and Hactar Go Lite. Please update your version!

Start analysis by selecting “Game Analysis” (or Ai icon) in SGF editor. If neither is visible, you probably have old Hactar Go version.

First whole game is analyzed using low iteration count. This offers overview to development of the game. It is also possible to move around in game using the analysis graph.

Red line is winrate, that is the probability that black would win the game. Black / White areas reflect the score difference between the players. In the middle the game is even. Each vertical line is 10 point advantage to the player. The text in the bottom gives precise numbers for these.

When visiting a move, Hactar makes more accurate analysis. The analysis shows loss of points compared to the best move (green). Smaller number is number of iterations used to analyze the move.

Selecting an analyzed position shows the best continuation, as if the move would be played.

Moves in variations are analyzed in the same way. Analysis starts visiting the move.


Exact number of iterations for analyzing a move is still work-in-progress, and will be adjusted later (probably up). The actual load from real-world use is a factor, as GPUs are not free, and one-time payments may not be the best way to cover running costs forever.

Initially all pro subscriptions get 3000 evaluations in analysis, while Hactar Go users without subscription get 500 evaluations.

Hactar Go Lite with other than pro subscriptions get 1000 evaluations. With no subscription Hactar Go Lite users get 40 evaluations. 40 evaluation for a move is not much, but time will show if it is feasible to offer anything for free at all.

GPUs in Cloud

Analysis uses same GPUs in cloud as Hactar’s pro-level opponent. There are at least two machines serving the requests, both with multiple GPUs. This provides reasonably reliable and scalable back-end.

GOWrite Update

Long time no see! GOWrite 3.0.5 is now stable version with Katago integration.

Katago integration does multple things.

The game trend is calculated using low iteration count. Red line is winrate, that is the probability that black would win the game. Black / White areas reflect the score difference between the players, in the middle the game is even. Each vertical line is 10 point advantage to the player. Text in bottom gives precise numbers for these.

The continuation moves are automatically evaluated and show with point difference compared to the best move, winrate for the move and iterations done as part of the search.

When mouse is above a continuation move, best continuation sequence is shown with numbered stones.

If you want to evaluate a continuation move not evaluated by default, you can ctrl-click the position to calculate evaluation for the continuation. To investigate the move more, you can add the move as variation.

About evaluations

Moves in variations are evaluated as they are visited. When analysing variations it may be helpful to enable “Insert” -> “Automatic variations”. With this, GOWrite will create a variation when adding a new move that is not last in the game. Handy for checking out a variation!


In GOWrite Katago configuration is in general settings. It is good practice to test Katago before attempting to use it. The test checks some of the known problematic settings in the configuration file and attempts to start Katago.

GOWrite does not include katago, its configuration or network, so they should be installed separately. Katago should be at least version 1.8, preferably latest.

Katago is used in its analysis mode. Katago analysis mode configuration example is analysis_example.cfg. The configuration may need adaptation for the hardware used. As GOWrite may do up to 3 parallel evaluations, the configuration should allows 3 concurrent evaluations.

Engine doing well

After playing over 1000 games in cgos server, Hactar-7-197-p is now first among recently playing engines. Version has two improvements over previous version.

Network is now somewhat better. Training has gone thru 197-181=16 generation, and this made visible improvement.

However, biggest change is thinking in opponents time (“pondering”, thus -p). It helps as it gives more network evaluations. Big impact is also when  normal search starts, pondering has created  small tree to start with.
This small tree helps as GPU is providing number of parallel evaluations to build MC tree, and getting sensible place for these evaluations  is possible as soon as there is small tree.

Testing smaller GPU

Most recent version Hactar-7-209a-p is using P4000 GPU instead of 1080ti; thus it is running with only half of the GPU power!

Still, it looks like Hactar-7-209a-p is almost as good as Hactar-7-197-p. This goes back to reducing latency of tree walk, NN evaluation, tree update cycle. Thus even if there is only half of the network evaluations, evaluations are a lot more relevant.

At some point I will make this improved engine to play using 1080ti. It will interesting to see its wining-%…

New engine playing

My first engine to play is now playing in 9×9 board. Engine is pretty typical AlphaZero trained engine with deep neural network. And name engine is Hactar, as this is intended for Hactar android app, although engine will be on cloud.

Currently engine is active on cgos bot server . At the moment Hactar is in 2nd  place among active bots, but in past there have been significantly stronger bots in the server. When Hactar is not playing anymore, past results can be found also from bayes estimation pages.

Engine is using one 1080ti card, CPU use is not significant.

The engine name “Hactar-7-181” contains also neural network model number (7) and the training iteration. For details of neural network and differences between versions, I will need to write another posting.