Download v1.1 (64-bit version)Download v1.1 (64-bit version)Orion 1.1 is now available!
After devoting a lot of time to Nostradamus, a chess engine that uses a large language model to learn and predict the optimal move for a given position, I finally found the time to enhance Orion!
Here is the new version of 'my little engine':
The gain in Elo should be quite small (something around 50+ Elo), but consistent in my tests with three different datasets mixing CCRL and/or CEGT games. However, I only had time to test at short time controls, so fingers crossed!
Most of the time for this release has been spent on the Cerebrum library. The library has been adapted to focus only on quantised networks, and predict values beyond the previous limited range of -1.98 to 1.98. This allows the networks to predict evaluations directly in terms of pawns and centipawns for direct use in engines. There is no need to mix different values on different scales (win ratio and material) anymore!
I discovered NotebookLM from Google last summer, and used it to generate a podcast from this blog. It's astonishing!
The Adventures of Tintin, the legendary Belgian comic series created by Hergé, has achieved a remarkable cultural footprint in India, evolving from translated comic books to a beloved Hindi-dubbed animated series. This transition allowed the stories of the young reporter and his loyal dog to transcend language barriers and reach millions of Hindi speakers across the subcontinent. The Hindi Adaptation and Cultural Shift
While Tintin first entered the Indian market through Bengali translations in the 1970s, it wasn't until 2010 that official Hindi comic book translations were launched by Om Books International. This "Indianization" process involved creative changes to make the characters more relatable:
Characters: Snowy became Natkhat, and the bumbling detectives Thomson and Thompson were renamed Santu and Bantu.
Voice Acting: The animated series featured prominent Indian voice artists, including those who brought a youthful, heroic tone to Tintin. Popular Hindi Episodes and Themes
The animated series, which originally debuted in 1991, consists of 39 episodes across three seasons. Several "full episodes" have gained significant popularity in Hindi, often highlighting Tintin's travels to or through the East: The Adventures Of TinTin [HINDI] by ANIME AND TOONS
Tintin In Hindi Full Episodes
The Adventures of Tintin is a beloved cartoon series that has been entertaining audiences for generations. The series follows the adventures of Tintin, a young Belgian reporter, and his loyal dog Snowy as they travel the world and solve mysteries.
Watch Tintin In Hindi Full Episodes Online
If you're looking to watch Tintin in Hindi, you're in luck! Here are some options to stream full episodes of Tintin in Hindi:
List of Tintin Episodes in Hindi
Here's a list of some popular Tintin episodes available in Hindi:
Watch Now and Enjoy!
So, what are you waiting for? Watch Tintin in Hindi full episodes online today and relive the excitement of Tintin's adventures!
Let me know if you want me to make any changes!
also here are some keywords that can be used for SEO purpose:
You can watch full episodes of The Adventures of Tintin dubbed in Hindi across several video-sharing and streaming platforms. Where to Watch Hindi Episodes
While official streaming availability for the Hindi dub can vary by region, here are the primary sources to find them: Dailymotion
: This is currently one of the most consistent places to find full Hindi-dubbed episodes organized by seasons. Season 1 Complete : Available on Sasta Cartoon Network's Dailymotion Playlist Season 2 & 3 : You can find playlists for on the same platform. Mixed Collections : Channels like ANIME AND TOONS also host several Hindi-dubbed adventures like The Secret of the Unicorn Red Rackham's Treasure Official Streaming Services Amazon Prime Video : Some regions offer The Adventures of Tintin animated series. However, availability of the Hindi audio track
specifically depends on your local library and licensing agreements. : The series is listed on
, but typically only features English and other European languages unless accessed from specific South Asian regions.
: While many full episodes are frequently removed due to copyright, search for "Tintin Hindi Full Episodes" to find community-uploaded clips or short-lived playlists. Prime Video Classic Hindi Adventures Available
Most Hindi-dubbed versions cover the 1991 animated series, including fan favorites such as: The Adventures of Tintin, Season 3 - Prime Video Prime Video: The Adventures of Tintin, Season 3. Prime Video
There is a common point of confusion when searching for "Tintin in Hindi full episodes." Many results will pull up the 2011 motion-capture film directed by Steven Spielberg and Peter Jackson: The Adventures of Tintin: The Secret of the Unicorn. Tintin In Hindi Full Episodes
While that movie is fantastic and was brilliantly dubbed in Hindi, it is not the full series.
If you want the nostalgic episodic adventures, stick to the 1991 series. If you want a cinematic thrill ride, watch the 2011 Hindi dub.
In an age of fast-paced Chhota Bheem and loud Doraemon, Tintin offers a slower, more intellectual burn. Watching Tintin in Hindi full episodes with your child offers distinct benefits:
Tintin, the adventurous Belgian reporter created by Hergé, has entertained generations with globe-trotting mysteries, eccentric characters, and clear-line comic art. If you’re looking for Tintin in Hindi full episodes, here’s a concise, practical guide covering what to expect, where to look, and legal/quality considerations.
What “Tintin in Hindi” typically means
Where to look (legal/official first)
Unofficial sources & cautions
Quality and completeness notes
How to search effectively
Episode list reference (major story arcs in the 1991 series)
Quick tips for best experience
If you want, I can:
Which follow-up would you like?
(Invoking related search terms for further exploration.)
The global phenomenon of The Adventures of Tintin has successfully transitioned into the Indian cultural landscape through dedicated Hindi dubs of the animated series and localized comic translations. Originally created by Belgian cartoonist Hergé in 1929, the series follows a young reporter and his loyal dog, Snowy, on high-stakes international adventures. Localized Character Identities
To resonate with Hindi-speaking audiences, many iconic characters were given culturally familiar names in official translations: : Known as (meaning "mischievous"). : Renamed , leaning into their roles as a bumbling, comedic duo. Professor Calculus : Reimagined as Professor Aryabhat Suryamukhi .
Captain Haddock's Slang: His famous insults were creatively adapted. For instance, "Ten thousand thundering typhoons" became "Dus hazaar tadtadate toofan". The Hindi Animated Series
The 1991 animated series is widely available in Hindi, often hosted on platforms like Dailymotion and YouTube. Popular episodes frequently sought in Hindi include:
The phenomenon of in Hindi is a fascinating case study in how Western media can be successfully "reimagined" for a completely different cultural landscape. While Hergé’s legendary reporter-sleuth originated in Belgium, his arrival on Indian television screens—specifically through the Hindi-dubbed versions of the 1990s animated series—turned him into a household name for a generation of Indian children. The Power of the Voice The success of
in Hindi wasn't just about the animation; it was about the localization of personality. In the Hindi version, the casting was impeccable. Tintin’s voice retained its youthful idealism, but the real star of the dub was often Captain Haddock.
The translation of Haddock’s iconic insults (originally "Billions of blue blistering barnacles!") into Hindi was a stroke of creative genius. Phrases like "Karodo Karod Kaale Kasht"
managed to capture the rhythmic, alliterative frustration of the Captain without losing the comedic essence. This linguistic adaptation made the characters feel like they belonged in an Indian living room rather than just being foreign imports. Cultural Resonance and Global Adventure For many Indian viewers, The Adventures of Tintin The Adventures of Tintin, the legendary Belgian comic
served as a first window into global geography and history. Because the Hindi dubbing was so seamless, the educational aspect felt like entertainment. Whether Tintin was navigating the streets of New Delhi in Tintin in Tibet
or exploring the moon, the Hindi narration made these high-stakes adventures accessible.
The "wholesome" nature of the show also appealed to the Indian middle-class values of the time. Tintin was a hero who didn't rely on superpowers, but on his wits, his loyalty to his friends, and his unwavering sense of justice—values that resonated deeply with Indian storytelling traditions. The Nostalgia of the "Full Episode"
Today, the search for "Tintin in Hindi full episodes" is driven largely by nostalgia. For the "90s kids" of India, these episodes represent a simpler time in broadcasting before the explosion of hyper-active, modern cartoons. The pacing of
was deliberate; it respected the viewer's intelligence and allowed the mystery to unfold.
Finding these full episodes on platforms like YouTube or DailyMotion has become a digital search for adults wanting to relive their childhood. The old TV recordings' grainy quality adds to the appeal, acting as a reminder of the early days of satellite television in India, especially during the time of networks like Gemini or Sahara. Conclusion Tintin in Hindi
is more than a dubbed cartoon. It connects European comic art with Indian pop culture. It showed that good storytelling is universal if told in a relatable language. Even after many years, the image of a small white dog and a red-haired reporter, accompanied by a Hindi-speaking Captain Haddock, remains a strong memory for many.
offer the series, language availability varies by region. For the classic Hindi dub, the following community-driven platforms are the most reliable: Dailymotion (Anime and Toons):
This channel hosts a significant portion of the series in Hindi, including major story arcs like The Secret of the Unicorn The Crab with the Golden Claws Dailymotion (Sasta Cartoon Network): Offers organized playlists for Season 1 in Hindi. YouTube Community Channels:
Various fan-uploaded channels occasionally host full episodes, though they are subject to copyright removal. Top Hindi Dubbed Episodes
The Hindi version is particularly praised for its adaptation of the following iconic adventures: Episode Title (English) Highlights The Secret of the Unicorn
The introduction of the Haddock lineage and the search for the treasure of Red Rackham. The Crab with the Golden Claws
The historic first meeting between Tintin and Captain Haddock. Cigars of the Pharaoh
A thrilling mystery set in Egypt and India, featuring the recurring villains of the series. The Black Island
A fast-paced mystery involving counterfeiters and a "monster" in Scotland. Cultural Impact in India Comic Books: Beyond the TV series, Pratham Books
has officially translated many of Hergé’s original 24 adventures into Hindi, making them accessible to a new generation of Indian readers. The "India" Connection: Indian fans often cite Cigars of the Pharaoh Tintin in Tibet
as favorites due to their geographic proximity and the respectful portrayal of South Asian cultures in the Hindi scripts. Viewing Tips Look for HD Remasters:
Some fan-uploaded versions on Dailymotion are labeled "HD" and offer significantly better visual quality than older TV recordings. Check Playlists:
To watch chronologically, search for "Tintin Hindi Playlist" on Dailymotion to avoid missing multi-part episodes. If you'd like, I can: Help you find specific episode links for a particular story arc. Detail the differences between the Hindi dub and the original comics Provide a list of official Hindi comic book titles for collectors. Let me know how you'd like to continue your Tintin journey
Finding full Hindi episodes of The Adventures of Tintin is easiest through community-shared platforms, as major streaming services in India currently have limited availability for the Hindi-dubbed version of the 1991 TV series. Where to Watch Online Dailymotion (Recommended):
This platform hosts the most complete collection of Hindi-dubbed episodes uploaded by fans. Season 1 Full Playlist: You can find the Complete Season 1 in Hindi Sasta Cartoon Network channel Season 2 Episodes: Available via this Hindi playlist including episodes like Tintin in Tibet Alternative Collection: Another large set of episodes is maintained by ANIME AND TOONS Prime Video: While the series is listed on Prime Video India
, it is frequently unavailable for streaming or only available in English depending on regional licensing. The 2011 Steven Spielberg film The Adventures of Tintin: The Secret of the Unicorn is available to watch in Hindi/Urdu on this platform. Dailymotion Popular Episodes in Hindi YouTube Channels: There are several YouTube channels that
If you are looking for specific fan favorites, these are widely available in the Hindi dub: The Crab with the Golden Claws first episode in Season 1. Cigars of the Pharaoh Featuring Tintin's adventure in India The Secret of the Unicorn A two-part mystery. Tintin in Tibet A high-altitude rescue mission. Search Tips
When searching for more episodes, use these specific Hindi keywords for better results: The Adventures of Tintin Hindi Dubbed Tintin Hindi Episodes Full
टिनटिन कार्टून हिंदी में all 39 episodes
in their chronological story order to help you find them more easily? The Adventures Of TinTin [HINDI] by ANIME AND TOONS
The Adventures Of TinTin [HINDI] by ANIME AND TOONS - Dailymotion. 1 Share Bookmark. ANIME AND TOONS. Dailymotion ANIME AND TOONS
The classic animated series, The Adventures of Tintin (1991), has a dedicated following in India, where the Hindi dub of the show became a childhood staple for many. For fans looking to relive these adventures, Tintin in Hindi full episodes are widely sought after for their nostalgic voice acting and localized dialogue. Where to Watch Tintin Hindi Full Episodes
While the series originally aired on television, it is now primarily available through digital archives and video-sharing platforms.
Internet Archive: A comprehensive resource for classic media, the Internet Archive hosts various episodes and seasons of the 1991 series, often including the Hindi dubbed versions in their community-uploaded collections.
Dailymotion: This platform is a popular hub for fans who upload individual segments or full episodes. Channels like ANIME AND TOONS have organized playlists containing Hindi dubbed versions of iconic episodes like "The Secret of the Unicorn" and "The Crab with the Golden Claws".
HindiCinemaHub: Some unofficial streaming sites, such as HindiCinemaHub, claim to host all three seasons (39 episodes) with Hindi audio in high definition.
BiliBili: International video communities like BiliBili often feature user-submitted full-length Hindi dubs of the 1991 series and the 2011 motion-capture film. The Hindi Voice Cast
The Hindi dub is celebrated for maintaining the distinct personalities of the original characters. The Adventures of Tintin (TV series) - Hindi Dubbing Wiki
While there isn't one single "post" that defines the Tintin experience in Hindi, the most interesting aspect of The Adventures of Tintin
in India is its legacy as a staple of 1990s and early 2000s television, particularly through its Hindi dub. The Nostalgia Factor
For many Indian millennials, Tintin wasn't just a comic book hero but a television icon. The Hindi dub is celebrated for its high-quality voice acting, which managed to preserve the European charm of Hergé’s world while making it accessible to a local audience.
Captain Haddock's Vocabulary: One of the most beloved parts of the Hindi episodes is how Haddock's iconic insults (like "Blistering Barnacles!") were creatively translated into Hindi phrases that captured his explosive yet harmless temper.
The Voice of Tintin: The Hindi voice for Tintin is often remembered for being calm, sharp, and inquisitive, perfectly matching the reporter's persona. Where to Find Full Episodes
If you are looking to revisit the series or introduce it to someone new, "The Adventures of Tintin" in Hindi can be found across several digital platforms:
YouTube: Various official and fan-curated channels host full episodes. Many fans have uploaded the original 1990s Hindi dubs, which are often considered superior to later re-dubbings.
OTT Platforms: Depending on your region, platforms like Netflix or Amazon Prime Video occasionally carry the series, though language options vary by license.
Physical Media: For collectors, DVD box sets of the Hindi-dubbed Nelvana series (1991–1992) remain a popular item for those seeking the original broadcast quality. Impact on Indian Pop Culture
The "Tintin in Hindi" episodes played a significant role in popularizing Franco-Belgian comics in India. Because the show aired on major networks like Cartoon Network and Doordarshan, it reached millions of children who might not have had access to the expensive imported comic books at the time.
Amazon Prime Video has offered the complete animated series in various languages in the past. Indian users should search specifically for "Tintin" and check the audio settings for Hindi. The availability rotates, but it is the best legal HD option when active.
Orion 0.9 is available !
More than one year since the last release already: time flies ! I have been very busy these last months, without a lot of time to dedicate to Orion, but the next version is here (it is actually ready since... last November !), with:
Support for SMP
I'm really happy to announce the support for SMP : Orion will now be able to think using several CPUs/threads in parallel, hopefully resulting in a stronger play ;-)
This required a lot, what am I saying, a ** LOT ** of work: I had to redesign the main parts of the engine, to ensure thread-safe execution, split, refactor, simplify, rearrange the code to avoid problems when computing in parallel. On the contrary, I was surprised by the simplicity of the Lazy SMP approach, that's brilliant !
Smaller network architecture
The other big change is the architecture of the neural network: it is now much simpler than the previous one, for a more or less equivalent strength (~20-30 elo weaker in my own tests). I replaced the 40960x2 weights in the first layer by a simple 768x2 scheme (6 types of piece x 2 colors x 64 squares = 768).
This probably hampers accuracy in some complex positions, but globally speeds up evaluation as you don't have to recompute all the first layer when the king is moving (this is really helpful in endgame positions where kings have more mobility). This choice resulted in a 24x smaller network (421 kB vs 10 MB)...!
I'm really happy with the result: It seems possible to compress chess knowledge a lot !
Important note about originalityI know that some people are looking for originality: do not forget that engine creation can remain for some of us (including me) a hobby and/or a way to learn programming and A.I. !
This has always been my goal: develop a 100% original engine, not only in terms of playing style (that's not the case for the moment) but also in terms of code: Orion is not derived from any other engine, I wrote 100% of the lines of code, in my own way, always after having taken the time to understand what I was doing (the most recent example being the NNUE experiments I led in 2020).
For example, Orion is based on a "only-legal" move generator, using flags embedded in each move representation to help sorting and pruning moves during search. Its transposition table also uses the number of pieces on the board as a criterium to replace old-entries.
But then comes the issue of the data used to train the neural network with the NNUE approach.
As for the 0.8 version, the provided network has been trained on positions that were statically evaluated with the nn-82215d0fd0df.nnue network which is the one embedded in StockFish 12. The StockFish engine itself was not used at all in that process: I took the network, reused the code I developed for my NNUE experiments to read the weights and evaluate a bunch of positions that I collected from CCRL games, and then trained my own network with my own (shared) Cerebrum library (note: this time, I was able to use only 128 million of positions, compared to the 360 million used for 0.8).Finally, from this perspective, I think one cannot consider that Orion is - at this stage - a 100% original work, as it uses knowledge coming from another engine. Please note that starting from v0.4, it has actually been the case: I was previously using StockFish 8 (static) evaluations to tune parameters of my handcrafted evaluation function.
But, for sure, this remains the goal, and I already started to work on reaching that objective...
The "zero" approachI think the most exciting challenge now that I know how to design and train neural networks is to find a way to train a network from zero, i.e. only using results of games (win / draw / loss). Inspired by an idea proposed by Connor McMonigle (Seer author), I tried to train one of such network, without success so far.
The idea is to consider endgame positions (3-4-5-6 pieces), use the results provided by Syzygy tablebases, train a network on these positions, use the engine to evaluate 7-pieces positions with the trained network (after a depth 'd' search), re-train a new network on these labelled 3-to-7 pieces positions, and then restart all the process for 8 up to 32 pieces positions. The beauty of this approach is that the network is trained only using the endgame outcome, and shall learn how to "retropropagate" to middlegame positions the expected result.
Next stepsThis is my current effort: try to improve the way to train such a "from zero" neural network, only relying on game results. That's a very difficult challenge ! Be patient ;-)
Orion 0.8 is released !
I finally managed to build my own "neural network trainer", after a lot of experiments (see here) ! I'm now pleased to release a new version of my little engine Orion, where all the evaluation part relies (only) on a neural network !
Architecture
The architecture of the network used is "NNUE-like", but smaller and simpler than the one used by Stockfish 12 : I was very curious to see to which extent it was possible to "compress" chess knowledge without sacrifying too much strength.
After having tested several combinations, I finally found that halving (*) the first NNUE layer was a good compromise between the loss in strength and the gain in speed (which compensates).
Another change is that all dot products are performed on float values, which is a handicap in terms of speed but simpler from a training perpective. Values of the first layer are rounded and stored as 16-bit integers, resulting in a final 10 MB file.
Training data
Training was performed using 360 million unique positions, extracted from CCRL games, against the nn-82215d0fd0df.nnue network. This network has been released back in August in the public domain by Sergio Vieri, and is now embedded in Stockfish 12.
After 150 iterations ("epochs"), my own tests showed an increase of ~200 elo against v0.7, but this has yet to be confirmed (it is probably highly biased by the fact that I kept the same set of opponents).
The Cerebrum
To help other programmers to understand how to train and use neural networks, I decided to share my work through the "Cerebrum" library, composed of a trainer (Python script) and the corresponding inference code to be embedded in an engine (C langage). The trainer is a cleaned version of the one used for Orion, while the inference code is actually the one used in the engine. I hope all of this will be useful.
What's next ?
This version represents a lot of work. Understanding how neural networks work and how to train them was very challenging ! Now, the next challenge will be to "cut the link" with Stockfish's evaluation. The road is still long but, as we said in French, "Paris ne s'est pas faite en un jour" !
Credits
Credits and a big thank to Sergio Vieri for his incredible work, but also to Yu Nasu for the NNUE concept introduction, and following authors/creators who have worked on its implementation in Shogi and Stockfish (see list here). Last but not least, thanks to the CCRL team for providing games of their tournaments in such a simple way !
Final note
Syzygy support has been removed from this version.
(*) The network architecture is : 2x[40960x128 + 128] x [256x32 + 32] x [32x32 + 32] x [32x1 + 1], where "[W + B]" are the weights (W) and the biases (B).
Orion 0.8 is almost ready !
Next version should be released in a few days, if all goes as expected. In the meanwhile, here is how v0.7 performed in CCRL and CEGT lists : this corresponds more or less to a 110-130 elo increase from the v0.6 : I'm very happy !
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CCRL | 40/15 | 114 | 2761 | +25 | -25 | 516 |
| CCRL | 40/2 | 128 | 2736 | +17 | -17 | 1231 |
| CEGT | 40/4 | 121 | 2595 | +15 | -15 | 1350 |
(*) Time control (40/15 means 40 moves in 15 minutes)
Experiments with Neural Networks
I really don't have a lot of time these days, but due to the NNUE on-going 'revolution', and because I'm deeply convinced that this kind of approach is the future, I decided to play a bit with neural networks.
My experiments are related here. To date, I managed to get my own NNUE implementation, giving a serious boost in terms of elo performance (note that this version is purely experimental and shouldn't be considered as the official "Orion" : it is provided only for entertainement/experiments).
I'm currently trying to build a 'neural network trainer' to train my own networks, with the aim to build in a first attempt simpler networks than Stockfish's ones, and test if they can improve current v0.7 evaluation function.
Stay tuned !
Orion 0.7 is available !
Next weeks will be busy, and I won't have a lot of time to work on the SMP version. I prefer to release the new version now, which already includes a lot of rework. Main changes are described in the previous post. I forgot to mention that now Orion also embeds an handcrafted KPK bitbase... and a refreshed logo ;-) As regards Transposition Table (TT) ageing, I opted for a simple implementation : at the beginning of a search, TT is informed of how many pieces remain on board. Every TT entry which is already stored with a greater 'popcount' can be safely and unconditionnaly replaced. This seems sound, and gave good results during my own tests.
I hope the new version will reach a +100 elo increase (when using Syzygy tablebases), but that remains to be confirmed !
Happy birthday Orion ! Last version is just one year old, and performed relatively well with an increase of around 100-110 elo from the previous version : I'm very happy :-)
Next version is on good shape : I managed to achieve some good results, mainly thanks to the addition of Syzygy tablebases support. Some parts of the code have been totally rewritten, like evaluation, magic numbers generation, magic/BMI attacks computation, or static exchange evaluation (again !). Among various changes, aspiration window is finally working, Transposition Table is being "aged" (it was not the case until now, but I choose a different approach than other engines - more details to come), hash move is always tried in Quiescence (even if it's a quiet move which should have not been generated) and before move generation (speed gain), and, finally, PVS is also implemented in Quiescence (surprisingly, this does not seem to be common : maybe I'm doing something wrong). For evaluation tuning, I switched from genetic algorithms to pure linear regression (using Python scripts and Scikit-learn). Orion's evaluation has always been and is still... basic :-) At the moment, gain is between 50 (without Syzygy) and 100 (with) elo. I'm wondering whether to release or not the current development version, but at this stage, I would like to try to implement an important feature which is still missing : multi-CPU support (SMP) !
Current version strength (v0.6) :
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CCRL | 40/15 | 144 | 2635 | +20 | -20 | 819 |
| CCRL | 40/2 | 149 | 2624 | +17 | -17 | 1204 |
| CEGT | 40/4 | 141 | 2464 | +9 | -9 | 3200 |
(*) Time control (40/15 means 40 moves in 15 minutes)
Orion v0.6 is here !
Almost one year of work... :-) Main changes are :
In my testing conditions (5000 games, 4000 played at 40/1 + 1000 played at 40/2), this version should be ~100 elo stronger than v0.5.
En route for v0.7 and a new long-term objective: reach one day 2800 elo ?!
Orion v0.6 is almost ready to be released !
I'm currently running last tournaments to ensure non-regression with the very last build. It has been almost one year since the last post on this "blog": I worked hard on the new version, continuously trying to improve the engine, test after test... Sometimes, I wonder how other programmers do to improve so quickly their own engine, especially for 2500+ elo engines !
As a rule, and from the very beginning, I always refuse to watch other engines' evalution code. I only took inspiration in code related to search, but only for the parts I can understand and implement on my own. For example, aspiration window at root node is a concept that still doesn't work in Orion. I think I understood the idea, but something is still wrong. As far as evaluation is concerned, Orion's code is 100% original : I only took inspiration from well-known sites like CPW, blogs from other authors (a thought for Mediocre which seems to reborn !) and, for sure, forums (TalkChess being the one I read the more).
Most of the Orion v0.6 progress will come from the evaluation function: I added some concepts and the magic of genetics did the rest :-) In the meanwhile, I'm really proud of the v0.5 strength. This version has been a solid ground to build another release ! After almost one year from its release, here are its current rating:
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CCRL | 40/40 | 175 | 2529 | +15 | -15 | 1548 |
| CCRL | 40/4 | 176 | 2513 | +13 | -13 | 2348 |
| CEGT | 40/4 | 166 | 2352 | +13 | -13 | 1922 |
(*) Time control (40/4 means 40 moves in 4 minutes)
Orion v0.5 is available !
After several weeks of hard work, and a huge number of games played to test, test, and test again, I'm pleased to release a new version of my little engine Orion !
So, what's new ? Not a long list of new features, but a lot of code changes and rewrite :
Yes ! It worked ! I finally managed to improve strength using PBIL method (a big thank to Thomas Petzke) ! v0.5 is the first genetically modified version of Orion :-)
Gain can appear low, but I use a simple and straightforward fitness evaluation method : only compare score difference between Orion and Stockfish (v8). My previous attempts failed because of a bad initialisation of weights. Tuning only applied on evaluation terms, from 25 millions of unique positions extracted from CCRL 40/40 games and took ~ 8-10 hours. For the next release, I'll try to include search parameters but this will need to change fitness evaluation and run games : it should really take a lot of time !
Lastly, I tried to improve my testing framework. In previous versions, I only ran gauntlets against my 3 prefered partners : iCE, Lozza, and Madchess. I now run 4000 games against 20 engines, at 40 moves / 60 seconds, using the Hert500.pgn opening book. To preserve my computer, CPU is underclocked at 2.24 GHz. A complete run takes ~ 36 hours (7 games are run in parallel).
I hope all this work will be reflected in an elo gain in real conditions !
Orion v0.5 is approaching !
Since the release of v0.4, I have been working a lot to try to improve Orion, testing dozens of code changes and playing thousands of games. I finally started to get promising results a few weeks ago. I'm currently trying to grab a few more elos before releasing a new version.
In the meanwhile, I'm really satisfied to see that current version performs relatively well in tournaments (it is 60-100 elo stronger than v0.3 !). Compared to previous versions, v0.4 is clearly a strong and sound basis to try new ideas. You'll find in the table below an idea of its current strength. I'm really excited with the current development version : stay tuned !
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CCRL | 40/40 | 188 | 2447 | +24 | -24 | 581 |
| CCRL | 40/4 | 205 | 2420 | +18 | -19 | 1045 |
| CEGT | 40/4 | 185 | 2262 | +14 | -14 | 1550 |
(*) Time control (40/4 means 40 moves in 4 minutes)
Orion v0.4 is out !
I'm really happy to release this new version : I worked a lot on it, testing tens of versions, to finally get a version doing what it was intended to :-)
From the source code perspective, this version does not vary a lot from the previous : I only made small adjustements on search and fixed some pieces of code that didn't do what I expected to.
Evaluation was just modified to adjust rooks scoring. I gave a new chance to PBIL algorithm to improve it with no results. This time, I tried to minimize the difference between Orion and Stockfish v8 scores, but in real games, it didn't give better play.
So, what's new in this version ? Even if final code differences are small, there are some big changes:
In addition, a BMI2 version of the engine is now provided, giving a small speed bonus (+ 5%) on compatible systems.
Why releasing a new version now ? Because, even if evaluation has not been improved, my own tests show a clear progression against v0.3 : +/- 100 elo at 40/4 ! I hope this will be confirmed in real tournaments and longer time controls...
Have fun and do not hesitate to give me feedback !
It has been nearly a year since Orion was put online, and you will find in the table below a good idea of its level. I'm quite happy with these results (many thanks to all testers) ! In fact, the engine performed better than I expected. However, during last months, I tried to improve again the last version but faced difficulties... Developing a chess engine can really cause headaches !
I first tried to improve my evaluation function (using genetic algorithms) : it only allowed me to validate my PBIL framework as real strength was finally not increased...! After multiple attempts, I suspected pruning and reductions techniques had (bad) influence while trying to optimize evaluation.
I then started to inspect search tree implementation to decide what to deactivate, and found some bugs and pieces of code not doing what they ware intended to... Several hundreds games later, I also suspected problems in Transposition Table, notably on replacement strategies. I then tried multiple approaches... before being satisfied.
I'm here. And last results seem to go in the right direction, but it's too early to release a new version : a lot of work is still planned ! I want first to stabilize search tree implementation and then give a new chance to genetic algorithms to improve Orion's evaluation function. For the latter, I think I will disable pruning and reduction techniques to better converge to a good solution...
During all my efforts, I also found time to implement a BMI2 version of the engine, giving (on compatible systems) an incredible... +0% speed boost ! Another disappointment... and a new source of forthcoming debug sessions :-)
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CCRL | 40/40 | 203 | 2383 | +22 | -22 | 686 |
| CCRL | 40/4 | 209 | 2345 | +18 | -18 | 1187 |
| CEGT | 40/4 | 204 | 2162 | +13 | -13 | 1600 |
(*) Time control (40/4 means 40 moves in 4 minutes)
Orion v0.3 is now available !
I'm very happy to release this new version after several weeks of hard work. It (almost) consists in a complete rewrite of the previous version, in order to have a more readable and robust code, which should be a better basis for further enhancements. And code is not throwing anymore tons of warnings when compiling ;-)
Aside from rewriting, some features have been added, changed or removed :
Evaluation is unchanged. The new pruning techniques allow smaller search trees while adding some search instability. It results in less reliable moves in shallow depths, but should increase strength for longer time controls. I'm very impatient to see how it will behave in tournaments !
Next version will focus on evaluation enhancement with a PBIL framework already implemented and ready to be played with !
New Orion v0.2 ratings :
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CCRL | 40/40 | 203 | 2266 | +38 | -38 | 230 |
| CEGT | 40/4 | 1346 | 2105 | +25 | -25 | 600 |
(*) Time control (40/4 means 40 moves in 4 minutes)
Orion v0.2 participated in its first tournament ("Special Stars", organized by CCRL team) and finished in 4th place !
As it was my goal to compete with other engines, I'm very proud of it ;-)
First feedback from testers with computers that don't support 'popcnt' instruction show that the engine may crash : this problem has been fixed and a patched version of Orion v0.2 has been repackaged in the zip file (see download section).
This shows that we never test enough ! Thanks to all testers for their patience...
Please report any new problem here.
I'm pleased to announce the release of Orion v0.2 !
This new version includes :
All these features should improve the engine speed :-)
Please enjoy !
The CEGT team tested intensively Orion v0.1... playing 1100 games ! Here is the rating obtained :
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CEGT | 40/4 | 1360 | 2048 | +18 | -18 | 1100 |
(*) Time control (40/4 means 40 moves in 4 minutes)
After the last CCRL update (many thanks to all testers !), these are the ratings of Orion v0.1 :
| Site | TC (*) | Rank | Elo | Games | ||
|---|---|---|---|---|---|---|
| CCRL | 40/4 | 229 | 2167 | +39 | -39 | 248 |
| CCRL | 40/40 | 220 | 2194 | +116 | -108 | 30 |
(*) Time control (40/4 means 40 moves in 4 minutes)
Orion v0.1 is now listed in CCRL (in the "complete list" only, because It played less than 200 games) !
After 30 games played, Orion has been evaluated at 2194 elo. The error margin is quite big (+/- 116), but totally normal since only a few games were played. I think its real level is closer to 2078 :-)
I'm very happy and proud to release the first version of my UCI chess engine : Orion v0.1 !
I started to work on it several years ago, as a hobby, but decided to rewrite it entirely (and more seriously) at the beginning of the year, switching from Java (easy for prototyping) to C (easiest to distribute).
It includes :
My long term goal is to reach 2500 elo (one day ?!), but for the moment, this version seems to have, let's say, some room for improvement :-)
It's an 100% original work (no fork/derivative), a lot inspired by chessprogramming.wikispaces.com, and ideas taken from the excellent blogs of Jonatan Pettersson (Mediocre) and Thomas Petzke (iCE).
In order to use Orion, you will need a GUI like Arena.
Last but not least, many thanks to Graham for accepting Orion to enter the CCRL competition !
Please enjoy !
Orion is free : you can download and use/test it without limitation/restriction. The zip contains a Windows executable, a personal logo (astronomy is another passion), and a network file. You are allowed to redistribute it or its elements, on the absolute condition that you don't modify them. Sources of the engine are not included since development is in a too early stage. From v0.8, a part of Orion has been released under the MIT license ("The Cerebrum" library).
Download v1.1 (64-bit version)
Previous versions : Download v0.1 to v1.0 (64-bit versions)