#API bridges

[api-bridges-work-in-algo-trading]

API Bridges Work in Algo Trading

API Bridges are a crucial part of algorithmic trading, which allows trading platforms, brokers, and custom trading algorithms to work seamlessly together. They provide real-time data transfer and order execution, thus making the trading strategy more efficient, faster, and accurate. In this article, we will explain how API bridges work in algo trading and further explore their importance for traders and developers, especially in India.

What is algorithmic trading? Algorithmic trading is the use of computer algorithms to automatically execute trades based on pre-defined criteria such as market conditions, technical indicators, or price movements. Unlike manual trading, algorithmic trading allows traders to make faster decisions and execute multiple orders simultaneously, minimizing human error and maximizing potential profits.

Understanding API Bridges in Algo Trading API bridges are the connector layer between different software platforms through which they can communicate with each other. In algo trading, an API bridge is used to bridge your trading algorithm running from platforms like Amibroker, MetaTrader 4/5, or TradingView to the broker's trading system for automated execution of orders.

Important Functions of API Bridges in Algorithmic Trading Data Feed Integration: API bridges enable direct access to live market data by the algo trader, such as current stock prices, volumes, and order books, from the broker's system. This will serve as the basis of information that the algorithm should interpret for better decision-making. Once the algorithm determines a suitable trading opportunity, the API bridge sends the buy or sell order directly to the broker’s trading system. This process is automated, ensuring timely execution without manual intervention.

Backtesting: API bridges enable traders to backtest their algorithms using historical data to evaluate performance before executing real trades. This feature is particularly useful for optimizing strategies and reducing risks.

Risk Management: An effective API bridge helps implement the risk management protocol in trading algorithms, for example, stop-loss or take-profit orders. When specific conditions are met, such orders are automatically entered to eliminate emotional decision-making and loss. Trade Monitoring: The API bridge continuously monitors trade execution with real-time updates on orders, positions, and account balances. The traders stay informed and make adjustments in their algorithms.

ALSO READ

Why API Bridges are the Need of Algo Trading? Speed and Efficiency: API bridges allow high-frequency trading (HFT), which enables traders to execute thousands of trades per second with minimal delay. This speed is very important in fast-moving markets where timing is everything to profitability.

Customization: With custom-built algorithms interacting with a multitude of brokers through the API bridge, traders can personalize their strategies, thus being able to implement advanced trading strategies that otherwise would not be possible to manually implement.

Integration is smooth. API bridges enable traders to connect their favorite platforms, such as Amibroker or TradingView, with brokers like Angel One, Alice Blue, or Zerodha. In other words, traders can continue using the software they are familiar with while availing of the execution capabilities of the broker's platform.

Cost-Effective: In comparison to hiring a dedicated team of traders or using expensive proprietary systems, API bridges are more cost-effective for algo traders. They allow traders to use the power of automation without the high overhead costs.   Improved Risk Management: By automating risk controls, such as setting limits for loss and profits, the algorithmic system ensures that the trades are executed with minimal risk, thus helping traders in India and worldwide to manage the risk exposure better.

API Bridges Working with Popular Trading Platforms Amibroker: Amibroker is a more popular software used by algo traders for technical analysis and backtesting. The integration of Amibroker with API bridge enables traders to execute a strategy in real-time against their preferred broker's interface, which enriches trading experience.

MetaTrader MT4/MT5: MetaTrader is also a widely used platform for algorithmic trading. Through an API bridge, traders can link their trading robots (Expert Advisors) to brokers supporting the MT4 or MT5 platforms to automatically execute trades based on their algorithms.

TradingView: The most renowned trading view is a charting platform famous for its user-friendly interface and powerful scripting language called Pine Script. With an API bridge, users can send real-time trading signals to their brokers for the broker to execute.

The Best API Bridges for Algo Trading in India are by Combiz Services Pvt. Ltd.: Combiz Services Pvt. Ltd. provides customized API solutions that ensure seamless integration between brokers and trading platforms. Their API bridges support a wide range of trading platforms such as Amibroker, MetaTrader, and TradingView, which makes it a good option for Indian traders seeking flexibility and speed in algorithmic trading.

AlgoTrader: AlgoTrader provides an advanced algorithmic trading platform that supports integration with various brokers through API bridges. It is known for its scalability and high-speed trading capabilities, making it a favorite among professional traders.

Interactive Brokers API: Interactive Brokers offers a robust API that allows traders to link their algorithms directly to their trading platform. With a rich set of features such as market data feeds and execution capabilities, the Interactive Brokers' API bridge is highly regarded by the algo traders.

How to Set Up an API Bridge for Algo Trading

Select a Trading Platform and Broker: You may select Amibroker or MetaTrader as the trading platform. Then, go for a broker who gives access to APIs, such as Zerodha or Alice Blue. Connect API: Once you have made a selection of the above-mentioned platforms and broker, you must connect the API bridge with your algorithm in relation to the broker's system. In this step, generally, it involves configuration settings and keys of the APIs. Create or Select Algorithm: If you are a new algo trader, you can make use of pre-built strategies or create your own using programming languages like Python or AFL (AmiBroker Formula Language).

Backtest and test the algorithm: Before you deploy the algorithm, backtest it with historical data to ensure it performs as expected.

Monitor and Adjust: After you have deployed the algorithm, monitor its performance and make adjustments according to the changing market conditions.

Conclusion API bridges are a must-have tool in the world of algorithmic trading, providing smooth integration, faster execution, and improved risk management. Using Amibroker, MetaTrader, or TradingView platforms, API bridges make sure that your trading strategy is executed efficiently and effectively. The power of API bridges enables traders to stay ahead in the competitive world of algo trading and maximize opportunities in the Indian stock market.

For someone seeking a robust and highly customizable solution for algo trading needs, Combiz Services Pvt. Ltd. has the best API bridge services that guarantee seamless integration and faster trade execution.

[throwing-summers-at-the-walls]

rage hatred suffering

i chose a text to translate for the computer science translation class and the teacher said there should be no translation available so i checked and there was none and she approved my text. i sent my translation yesterday and today she replied and said theres a translation. that fucking page was translated since the last time i checked it like. 2 weeks ago ???

so now i found another text (probably harder to translate too) and im looking everywhere before sending it to her, like if i find anyone translated oracle's vm virtualbox user manual somewhere i will obliterate them

[jcmarchi]

Bridging the ‘Space Between’ in Generative Video

New Post has been published on https://thedigitalinsider.com/bridging-the-space-between-in-generative-video/

Bridging the ‘Space Between’ in Generative Video

New research from China is offering an improved method of interpolating the gap between two temporally-distanced video frames – one of the most crucial challenges in the current race towards realism for generative AI video, as well as for video codec compression.

In the example video below, we see in the leftmost column a ‘start’ (above left) and ‘end’ (lower left) frame. The task that the competing systems must undertake is to guess how the subject in the two pictures would get from frame A to frame B. In animation, this process is called tweening, and harks back to the silent era of movie-making.

Click to play. In the first, left-most column, we see the proposed start and end frame. In the middle column, and at the top of the third (rightmost) column, we see three prior approaches to this challenge. Lower right, we see that the new method obtains a far more convincing result in providing the interstitial frames. Source: https://fcvg-inbetween.github.io/

The new method proposed by the Chinese researchers is called Frame-wise Conditions-driven Video Generation (FCVG), and its results can be seen in the lower-right of the video above, providing a smooth and logical transition from one still frame to the next.

By contrast, we can see that one of the most celebrated frameworks for video interpolation, Google’s Frame Interpolation for Large Motion (FILM) project, struggles, as many similar outings struggle, with interpreting large and bold motion.

The other two rival frameworks visualized in the video, Time Reversal Fusion (TRF) and Generative Inbetweening (GI), provide a less skewed interpretation, but have created frenetic and even comic dance moves, neither of which respects the implicit logic of the two supplied frames.

Click to play. Two imperfect solutions to the tweening problem. Left, FILM treats the two frames as simple morph targets. Right, TRF knows that some form of dancing needs to be inserted, but comes up with an impracticable solution that demonstrates anatomical anomalies.

Above-left, we can take a closer look at how FILM is approaching the problem. Though FILM was designed to be able to handle large motion, in contrast to prior approaches based on optical flow, it still lacks a semantic understanding of what should be happening between the two supplied keyframes, and simply performs a 1980/90s-style morph between the frames. FILM has no semantic architecture, such as a Latent Diffusion Model like Stable Diffusion, to aid in creating an appropriate bridge between the frames.

To the right, in the video above, we see TRF’s effort, where Stable Video Diffusion (SVD) is used to more intelligently ‘guess’ how a dancing motion apposite to the two user-supplied frames might be – but it has made a bold and implausible approximation.

FCVG, seen below, makes a more credible job of guessing the movement and content between the two frames:

Click to play. FCVG improves upon former approaches, but is far from perfect.

There are still artefacts, such as unwanted morphing of hands and facial identity, but this version is superficially the most plausible – and any improvement on the state of the art needs to be considered against the enormous difficulty that the task proposes; and the great obstacle that the challenge presents to the future of AI-generated video.

Why Interpolation Matters

As we have pointed out before, the ability to plausibly fill in video content between two user-supplied frames is one of the best ways to maintain temporal consistency in generative video, since two real and consecutive photos of the same person will naturally contain consistent elements such as clothing, hair and environment.

When only a single starting frame is used, the limited attention window of a generative system, which often only takes nearby frames into account, will tend to gradually ‘evolve’ facets of the subject matter, until (for instance) a man becomes another man (or a woman), or proves to have ‘morphing’ clothing – among many other distractions that are commonly generated in open source T2V systems, and in most of the paid solutions, such as Kling:

Click to play. Feeding the new paper’s two (real) source frames into Kling, with the prompt ‘A man dancing on a roof’, did not result in an ideal solution. Though Kling 1.6 was available at the time of creation, V1.5 is the latest to support user-input start and end frames. Source: https://klingai.com/

Is the Problem Already Solved?

By contrast, some commercial, closed-source and proprietary systems seem to be doing better with the problem – notably RunwayML, which was able to create very plausible inbetweening of the two source frames:

Click to play. RunwayML’s diffusion-based interpolation is very effective. Source: https://app.runwayml.com/

Repeating the exercise, RunwayML produced a second, equally credible result:

Click to play. The second run of the RunwayML sequence.

One problem here is that we can learn nothing about the challenges involved, nor advance the open-source state of the art, from a proprietary system. We cannot know whether this superior rendering has been achieved by unique architectural approaches, by data (or data curation methods such as filtering and annotation), or any combination of these and other possible research innovations.

Secondly, smaller outfits, such as visual effects companies, cannot in the long term depend on B2B API-driven services that could potentially undermine their logistical planning with a single price hike – particularly if one service should come to dominate the market, and therefore be more disposed to increase prices.

When the Rights Are Wrong

Far more importantly, if a well-performing commercial model is trained on unlicensed data, as appears to be the case with RunwayML, any company using such services could risk downstream legal exposure.

Since laws (and some lawsuits) last longer than presidents, and since the crucial US market is among the most litigious in the world, the current trend towards greater legislative oversight for AI training data seems likely to survive the ‘light touch’ of Donald Trump’s next presidential term.

Therefore the computer vision research sector will have to tackle this problem the hard way, in order that any emerging solutions might endure over the long term.

FCVG

The new method from China is presented in a paper titled Generative Inbetweening through Frame-wise Conditions-Driven Video Generation, and comes from five researchers across the Harbin Institute of Technology and Tianjin University.

FCVG solves the problem of ambiguity in the interpolation task by utilizing frame-wise conditions, together with a framework that delineates edges in the user-supplied start and end frames, which helps the process to keep a more consistent track of the transitions between individual frames, and also the overall effect.

Frame-wise conditioning involves breaking down the creation of interstitial frames into sub-tasks, instead of trying to fill in a very large semantic vacuum between two frames (and the longer the requested video output, the larger that semantic distance is).

In the graphic below, from the paper, the authors compare the aforementioned time-reversal (TRF) method to theirs. TRF creates two video generation paths using a pre-trained image-to-video model (SVD). One is a ‘forward’ path conditioned on the start frame, and the other a ‘backward’ path conditioned on the end frame. Both paths start from the same random noise. This is illustrated to the left of the image below:

Comparison of prior approaches to FCVG. Source: https://arxiv.org/pdf/2412.11755

The authors assert that FCVG is an improvement over time-reversal methods because it reduces ambiguity in video generation, by giving each frame its own explicit condition, leading to more stable and consistent output.

Time-reversal methods such as TRF, the paper asserts, can lead to ambiguity, because the forward and backward generation paths can diverge, causing misalignment or inconsistencies. FCVG addresses this by using frame-wise conditions derived from matched lines between the start and end frames (lower-right in image above), which guide the generation process.

Click to play. Another comparison from the FCVG project page.

Time reversal enables the use of pre-trained video generation models for inbetweening but has some drawbacks. The motion generated by I2V models is diverse rather than stable. While this is useful for pure image-to-video (I2V) tasks, it creates ambiguity, and leads to misaligned or inconsistent video paths.

Time reversal also requires laborious tuning of hyper-parameters, such as the frame rate for each generated video. Additionally, some of the techniques entailed in time reversal to reduce ambiguity significantly slow down inference, increasing processing times.

Method

The authors observe that if the first of these problems (diversity vs. stability) can be resolved, all other subsequent problems are likely to resolve themselves. This has been attempted in previous offerings such as the aforementioned GI, and also ViBiDSampler.

The paper states:

‘Nevertheless [there] still exists considerable stochasticity between these paths, thereby constraining the effectiveness of these methods in handling scenarios involving large motions such as rapid changes in human poses. The ambiguity in the interpolation path primarily arises from insufficient conditions for intermediate frames, since two input images only provide conditions for start and end frames.

‘Therefore [we] suggest offering an explicit condition for each frame, which significantly alleviates the ambiguity of the interpolation path.’

We can see the core concepts of FCVG at work in the schema below. FCVG generates a sequence of video frames that start and end consistently with two input frames. This ensures that frames are temporally stable by providing frame-specific conditions for the video generation process.

Schema for inference of FCVG.

In this rethinking of the time reversal approach, the method combines information from both forward and backward directions, blending them to create smooth transitions. Through an iterative process, the model gradually refines noisy inputs until the final set of inbetweening frames is produced.

The next stage involves the use of the pretrained GlueStick line-matching model, which creates correspondences between the two calculated start and end frames, with the optional use of skeletal poses to guide the model, via the Stable Video Diffusion model.

GlueStick derives lines from interpreted shapes. These lines provide matching anchors between start and end frames in FCVG*.

The authors note:

‘We empirically found that linear interpolation is sufficient for most cases to guarantee temporal stability in inbetweening videos, and our method allows users to specify non-linear interpolation paths for generating desired [videos].’

The workflow for establishing forward and backward frame-wise conditions. We can see the matched colors that are keeping the content consistent as the animation develops.

To inject the obtained frame-wise conditions into SVD, FCVG uses the method developed for the 2024 ControlNeXt initiative. In this process, the control conditions are initially encoded by multiple ResNet blocks, before cross-normalization between the condition and SVD branches of the workflow.

A small set of videos are used for fine-tuning the SVD model, with most of the model’s parameters frozen.

‘The [aforementioned limitations] have been largely resolved in FCVG: (i) By explicitly specifying the condition for each frame, the ambiguity between forward and backward paths is significantly alleviated; (ii) Only one tunable [parameter is introduced], while keeping hyperparameters in SVD as default, yields favorable results in most scenarios; (iii) A simple average fusion, without noise re-injection, is adequate in FCVG, and the inference steps can be substantially reduced by 50% compared to [GI].’

Broad schema for injecting frame-wise conditions into Stable Video Diffusion for FCVG.

Data and Tests

To test the system, the researchers curated a dataset featuring diverse scenes including outdoor environments, human poses, and interior locations, including motions such as camera movement, dance actions, and facial expressions, among others. The 524 clips chosen were taken from the DAVIS and RealEstate10k datasets. This collection was supplemented with high frame-rate videos obtained from Pexels. The curated set was split 4:1 between fine-tuning and testing.

Metrics used were Learned Perceptual Similarity Metrics (LPIPS); Fréchet Inception Distance (FID); Fréchet Video Distance (FVD); VBench; and Fréchet Video Motion Distance.

The authors note that none of these metrics is well-adapted to estimate temporal stability, and refer us to the videos on FCVG’s project page.

In addition to the use of GlueStick for line-matching, DWPose was used for estimating human poses.

Fine-tuning tool place for 70,000 iterations under the AdamW optimizer on a NVIDIA A800 GPU, at a learning rate of 1×10-6, with frames cropped to 512×320 patches.

Rival prior frameworks tested were FILM, GI, TRF, and DynamiCrafter.

For quantitative evaluation, frame gaps tackled ranged between 12 and 23.

Quantitative results against prior frameworks.

Regarding these results, the paper observes:

‘[Our] method achieves the best performance among four generative approaches across all the metrics. Regarding the LPIPS comparison with FILM, our FCVG is marginally inferior, while demonstrating superior performance in other metrics. Considering the absence of temporal information in LPIPS, it may be more appropriate to prioritize other metrics and visual observation.

‘Moreover, by comparing the results under different frame gaps, FILM may work well when the gap is small, while generative methods are more suitable for large gap. Among these generative methods, our FCVG exhibits significant superiority owing to its explicit frame-wise conditions.’

For qualitative testing, the authors produced the videos seen at the project page (some embedded in this article), and static and animated† results in the PDF paper,

Sample static results from the paper. Please refer to source PDF for better resolution, and be aware that the PDF contains animations which can be played in applications that support this feature.

The authors comment:

‘While FILM produces smooth interpolation results for small motion scenarios, it struggles with large scale motion due to inherent limitations of optical flow, resulting in noticeable artifacts such as background and hand movement (in the first case).

‘Generative models like TRF and GI suffer from ambiguities in fusion paths leading to unstable intermediate motion, particularly evident in complex scenes involving human and object motion.

‘In contrast, our method consistently delivers satisfactory results across various scenarios.’Even when significant occlusion is present (in the second case and sixth case), our method can still capture reasonable motion. Furthermore, our approach exhibits robustness for complex human actions (in the last case).’

The authors also found that FCVG generalizes unusually well to animation-style videos:

Click to play. FCVG produces very convincing results for cartoon-style animation.

Conclusion

FCVG represents at least an incremental improvement for the state-of-the-art in frame interpolation in a non-proprietary context. The authors have made the code for the work available on GitHub, though the associated dataset has not been released at the time of writing.

If proprietary commercial solutions are exceeding open-source efforts through the use of web-scraped, unlicensed data, there seems to be limited or no future in such an approach, at least for commercial use; the risks are simply too great.

Therefore, even if the open-source scene lags behind the impressive showcase of the current market leaders, it is, arguably, the tortoise that may beat the hare to the finish line.

* Source: https://openaccess.thecvf.com/content/ICCV2023/papers/Pautrat_GlueStick_Robust_Image_Matching_by_Sticking_Points_and_Lines_Together_ICCV_2023_paper.pdf

† Requires Acrobat Reader, Okular, or any other PDF reader that can reproduce embedded PDF animations.

First published Friday, December 20, 2024

[beeingapis]

"Hive with Care"

[watchnrant]

Interior Chinatown: A Sharp Satire That Challenges Stereotypes and Forces Self-Reflection

Interior Chinatown is a brilliant yet understated reflection of the world—a mirror that exposes how society often judges people by their covers. The show captures this poignantly with the scene where Willis Wu can’t get into the police precinct until he proves his worth by delivering food. It’s a powerful metaphor: sometimes, if you don’t fit the mold, you have to prove your value in the most degrading or unexpected ways just to get a foot in the door. The locked precinct doors represent barriers faced by those who don’t match the “majority’s” idea of what’s acceptable or valuable.

While the series centers on the Asian and Pacific Islander (API) community and the stereotypical roles Hollywood has long relegated them to—background extras, kung fu fighters—it forces viewers to confront bigger questions. It makes you ask: Am I complicit in perpetuating these stereotypes? Am I limiting others—or even myself—by what I assume is their worth? It’s not just about API representation; it’s about how society as a whole undervalues anyone who doesn’t fit neatly into its preferred narrative.

The show can feel confusing if you don’t grasp its satirical lens upfront. But for me, knowing the context of Charles Yu’s original book helped it click. The production team does an incredible job balancing satire with sincerity, blurring the line between real life and the exaggerated Hollywood “procedural” format. They cleverly use contrasting visuals and distinct camera work to draw you into different headspaces—Hollywood’s glossy expectations versus the grittier reality of life.

Chloe Bennet’s involvement (real name Chloe Wang) ties into the show’s themes on a deeply personal level. She famously changed her last name to navigate Hollywood, caught in the impossible middle ground of not being “Asian enough” or “white enough” for casting directors. It’s a decision that sparks debate—was it an act of survival, assimilation, or betrayal? But for Bennett, it was about carving a space for herself to pursue her dreams.

This theme echoes in one of the show’s most poignant scenes, where Lana is told, “You will never completely understand. You’re mixed.” It’s a crushing acknowledgment of the barriers that persist, even when you’re trying to bridge divides. Lana’s story highlights how identity can be both a strength and an obstacle, and the line serves as a painful reminder of the walls society creates—externally and internally.

Interior Chinatown doesn’t just ask us to look at the system; it forces us to examine ourselves. Whether it’s Willis Wu at the precinct door or Lana trying to connect in a world that sees her as neither this nor that, the show unflinchingly portrays the struggle to belong. And as viewers, it challenges us to question our role in those struggles: Are we helping to dismantle the barriers, or are we quietly reinforcing them?

[lucenare]

is there a public mod list for terrimortis? purely curious whats all in there

I started to answer this on mobile and then it was bothering me so its laptop time. theres a couple notes for things but i can answer and questions about it. this is roughly it, excluding any libs an apis

Main Mods:

Ammendments

Armourer's Worskshop - lets us build cool cosmetics (Ezra's wheelchair, all the antenae, leopolds legs, etc)

Beautify Refabricated

Chipped

Clumps

Collective

Incendium

Indium

Structory

Supplementaries

Another Furniture

Better Furniture

Cluttered

Comforts

Convenient Name Tags

Cosmetic Name Tags - How we change our names!

Croptopia

Joy of Painting - pai n t in g mo d

Dark Paintings

Ferritecore

Lithium

Mighty Mail - mailboxes!!

Origins

World Edit

Spark

Trinkets

Stellarity

Twigs

Villager Names - note, makes it so wandering traders dont despawn

Building Wands

Macaws:

Bridges

Doors

Fences

Trapdoors

Lights and Lamps

Paths and Pavings

Windows

Clientside:

Build Guide

Entity Model Features - Lets us use custom models like with optifine

Entity Model Textures

CIT Resewn - allows us to use optifine packs

Continuity - connected textures

Iris Shaders

Jade - lil pop up when you hover over blocks

Jade Addons

JEI

Skin Layers 3D

Sodium

Xaero's Minimap

Xaero's World Map

Zoomify

Chiseled bookshelf visualizer

Cull Leaves

Custom Stars

JER

LambDynamicLights

More Mcmeta

More Mcmeta Emissive Texures

Sodium Extras

Bobby

[slainesthrone]

So trying to recreate the qsmp mos pack (Personal use ig, just sawa bunch of mods I've never seen before and when Oh Shit thats exciting, then just diecided "Fuck it gonna gather as many of the mods they're using)

I Do Not have any of knowledge of any of the dungeons that they have so I ask if you have any insight let me know.

Here's the full list I have of confirmed mods and possible mods;

server runs on 1.20.1 qsmp 2024 mods: 1.Regions Unexplored 2.Croptopia 3.Biomes o plenty 4.born in chaos 5.exotic birds 6.enmey expansion 7.chocobo mod 8.farmers delight + some other food mods (Possibly multiple) with delight in the name. 9.Candlelight(?) 10.Handcrafted 11.Alex's mobs 12.Alex's caves (I can confirm because of a TRAP DOOR in the egg bakery. I'm in the trenches) 13.supplementaries 14.Beachparty 15.create 16.journey maps (Idk some map mod) 17.aquaculture 2 18.cluttered 19.chimes 20.fairy lights 21.FramedBlocks 22.Chipped 23.paraglider 24.Another furniture mod 25.waystones 26.connected glass 27.Create deco 28.Candlelight dinner 29.MOA DECOR 30.Tanuki decor 31.Orcz 32.Modern life 33.Bakery 34.Friends&Foes 35.Meadow 36.Abyssal decor 37.Twigs 38.lootr 39.when dungeons arise(to be confirmed) 40.nether's delight 41.rats 42.Additional lanterns 43.Alex's delight 44.Additional Lights 45.AstikorCarts Redux 46.Athena 47.Awesome dungeon net..(work?) 48.BOZOID 49.Apothic Attributes 50.AppleSkin 51.Balm 52.Better Archeology 53.Better ping Display 54.BetterF3 55.Aquaculture Delight 56.Bookshelf 57.Bygone Nether 58.CC: Tweaked 59.Artifacts 60.Camera Mod 61.Cataclysm Mod 62.Catalogue 63.Citadel 64.Cloth config v10 API 65.Clumps 66.Comforts 67.Configured 68.Controlling 69.CorgiLib 70.CoroUtil 71.Corpse 72.CosmeticArmorReworked 73.Create : Encased 74.Create Confectionery 75.Create Slice & Dice 76.Create: Interiors 77.Create: Steam 'n' Rails 78.Create: Structures 79.CreativeCore 80.Creeper Overhaul 81.Cristel Lib 82.Cupboard Utilities 83.Curios API 84.Customizable Player M(???) 85.Delightful 86.Distant Horizons 87.Domestication Innovations 88.Duckling 89.Dynamic Lights 90.Elevator Mod 91.Embeddium 92.Emotecraft 93.Enderman Overhaul 94.EntityCulling 95.Nether's exoticism 96.YUNG's (x) Mods (bridges, better dessert temples,mineshafts only ones i can confrim, might be all but idk for sure) 97.Securitycraft 98.Vinery (Confirmed because of Tubbo's drinking binge at spawn yesterday) 99.Mr.Crayfish (Furniture confirmed, possibly more) 100.Naturalist 101.Tom's simple storage

If you know or noticed mods that haven't been listed, reply/reblog with them please.

Things are numbered for my archival reasons, as some mods come in multiple separate mods (such as YUNG's) the numbering will not show the true number of the mods on the server.

I also have not checked the needed mods that any of these mods may need so.

(Please note that there may be spelling/grammar mistakes in the names of this mods!)

[cementcornfield]

an unfortunately hilarious way to describe how how the bengals fumbled this deal. listen here

transcription (and some commentary) below

they tried friday night, they even tried on saturday into saturday night, and it continued into sunday morning actually. they could not bridge the gap, it just was not happening. there were numbers that they couldn’t figure out. there was a cash flow issue, there was a years issue. the APY was fine. it would have surpassed justin jefferson but everything else was not there. and basically, this is exactly what justin jefferson went through last year. justin jefferson also wanted to get extended after his third year, the vikings could not bridge the gap. they made him play out his fourth year and eventually it all worked out and he just became the highest paid receiver after playing four years. ja’marr’s gonna go through the same thing. he’s gonna play through his fourth year and eventually at this time next year he’s gonna try to get extended and he was looking for 36 million a year right now. if he has a ja’marr chase type of year right now, ja’marr will be able to ask for 40 million dollars a year next year and he’s going to get it from the bengals. thats where it is. 

again, i don’t know why some of these organizations decide to wait when you have the star players, just pay them because if you don’t it gets more expensive…i’ll tell you this much, ja’marr’s side? not happy about it. ja’marr himself? not happy about it as well. he thought the deal would get done, this has been four, maybe even five, months of negotiations and they thought they were on the doorstep a couple of times only for it to collapse, and we’ve said it a million times - the bengals do negotiations way different than every other team. they're way more annoying about it and this player experienced it for the first time and he’s not happy about it. but there’s nothing he can do about it, he’s gonna play through the year. he handled it with class and he’s onto playing week 2 where he should be fully healthy. 

Okay so! pretty revealing stuff!

the cash flow thing. upon further research, this isn't the classic "oh boohoo the brown family is too poor!" but actually more about guaranteed money. it's cash flow for the player throughout the deal. the bengals tend to only guarantee a certain amount in the first (and very rarely the second) year of the deal. ja'marr's camp wanted more guaranteed money throughout the contract (which basically ensures security throughout the whole thing rather than risk being cut at the end, and could also prevent him from wanting to hold out/in because he wouldn't be making much per game towards the end). it seems like the bengals had a limit on the money they'd guarantee throughout six years, not because they couldn't, but because they wouldn't. (they are billionaires and are more than capable of drumming up more liquid cash if they really wanted to, but they don't want to.)

one thing i DO find comforting though is that we are not the only team to fuck this up lol. justin tried for an extension last year, and they also couldn't bridge the gap, making him play his fourth year first. and it all ended happily, he is the highest paid receiver now. and next year, ja'marr will be.

but! ja'marr is NOT happy right now. which sucks! he's a pro and i know he can put it behind him and focus on the game. but it's just such a bummer. hopefully he can use this as motivation and play pissed off/with a chip on his shoulder/whatever, but still, annoying it got to this point!

the confirmation about how annoying the bengals are as an organization to deal with from every single agent. it really shouldn't be a secret after jessie, tee, etc. and it's such a poor reflection on us that we supposedly didn't pay those guys to pay ja'marr, and we couldn't even get that right (we will eventually, but they've already used up any good will they had from ja'marr's side, and are going to have to pay way more now!)

[foone]

Two ideas:

1. I wonder if anyone has tried to build a Tumblr-a-like on ActivityPub?

2. I wonder if you could build a Tumblr/ActivityPub bridge out of the existing Tumblr API? Like I know Tumblr has said they plan to eventually make Tumblr part of ActivityPub, but you could always go "fuck you" and do it for them.

[bluemanedhawk]

I want to make this piece of software. I want this piece of software to be a good piece of software. As part of making it a good piece of software, i want it to be fast. As part of making it fast, i want to be able to paralellize what i can. As part of that paralellization, i want to use compute shaders. To use compute shaders, i need some interface to graphics processors. After determining that Vulkan is not an API that is meant to be used by anybody, i decided to use OpenGL instead. In order for using OpenGL to be useful, i need some way to show the results to the user and get input from the user. I can do this by means of the Wayland API. In order to bridge the gap between Wayland and OpenGL, i need to be able to create an OpenGL context where the default framebuffer is the same as the Wayland surface that i've set to be a window. I can do this by means of EGL. In order to use EGL to create an OpenGL context, i need to select a config for the context.

Unfortunately, it just so happens that on my Linux partition, the implementation of EGL does not support the config that i would need for this piece of software.

Therefore, i am going to write this piece of software for 9front instead, using my 9front partition.

[arysguide]

Part 1: Ary's favourite releases of 2023

Before you ask, yes I know that Mitski and Sufjan Stevens released albums this year! I'm gonna go ahead and assume they're already on a lot of other people's lists! However, if you think I'm missing out on YOUR favourite album of 2023, let me know. If you're thinking: "63 albums isn't that many, I wish there were more" - you're in luck because there's a Part 2. Part 2 has a different (more pop? upbeat? accessible?) vibe. Don't think too hard about it...

The chart isn't ranked, just arranged in a way that looked nice to me. Metal, hardcore, rap, emo, skramz, bedroom pop and more!!!

Here are Bandcamp links to all of the albums (for those not on BC there's a YouTube or Spotify link). Honestly I'm never quite sure what genre something is, but there's a lot of metal in any case.

Row 1

Lauren Bousfield - Salesforce [digital hardcore]

Dead Times - Dead Times [harsh noise extreme metal]

Danny Brown - Quaranta [rap/hiphop]

Underdark - Managed Decline [post black metal]

Boris & Uniform - Bright New Disease [psychedelic heavy metal]

PUPIL SLICER - BLOSSOM [blackened mathcore]

Sanguisugabogg - Homicidal Ecstasy [death metal]

Row 2

Full of Hell & Primitive Man - Suffocating Hallucination [death metal/grindcore]

Radeloos//Ziedend - Doodsverachting [blackened crust]

Agriculture - Agriculture [ecstatic black metal]

Victory Over the Sun - Dance You Monster To My Soft Song! [progressive black metal]

fog lake - midnight society [bedroom pop]

Bell Witch - Future's Shadow Part 1: The Clandestine Gate [funeral doom]

Krallice - Mass Cathexis 2 - The Kinetic Infinite [progressive black metal]

Row 3

Svalbard - The Weight Of The Mask [postmetal]

Terzij de Horde & Ggu:ll - Van Grond [vitalistic black metal]

portrayal of guilt - Devil Music [blackened post-hardcore]

SAINT VEHK - Practice​/​Doubt I&II [occult death industrial]

Sightless Pit - Lockstep Bloodwar [dub/power electronics]

Designer Violence - We Gave Peace A Chance [electropunk]

geronimostilton - The Vampyre [skramz]

Row 4

Chat Pile & Nerver - Brothers in Christ [sludgey death metal]

Afsky - Om hundrede år [depressive black metal]

Full of Hell & Gasp - FOH/Gasp (Split) [death metal/grindcore]

Solar Temple - The Great Star Above Provides [blackgaze]

Fluisteraars - De Kronieken Van Het Verdwenen Kasteel - II - Nergena [atmospheric black metal]

Fluisteraars - De Kronieken van het Verdwenen Kasteel - I - Harslo [atmospheric black metal]

Andre 3000 - New Blue Sun [spiritual flute jazz]

Row 5

Aesop Rock - Integrated Tech Solutions [rap/hiphop]

Blood Incantation - Luminescent Bridge [cosmic death metal]

Reverend Kristin Michael Hayter (fka LINGUA IGNOTA) - SAVED! [experimental gospel metal]

Spetterpoep - Stoelgang Van Zaken [coprogrind/grindcore]

Gnaw Their Tongues - The Cessation Of Suffering [blackened drone metal]

JPEGMAFIA & Danny Brown - SCARING THE HOES [rap/hiphop]

The Lemon Twigs - Everything Harmony [70s inspired rock]

Row 6

Old Nick - "The Truest Spell" [dungeon synth/raw black metal]

Armand Hammer - We Buy Diabetic Test Strips [rap/hiphop]

Liturgy - 93696 [transcendental black metal]

Helena Hauff - fabric presents Helena Hauff [hardcore techno]

That Same Street ⁻ Electric Angel [skramz]

That Same Street - Endgame [skramz]

the scary jokes - Retinal Bloom [dream pop]

Row 7

Bull of Apis Bull of Bronze - The Fractal Ouroboros [occult black metal]

Katie Dey - never falter hero girl [hyperpop]

Full of Hell & Nothing - When No Birds Sang [grindcore/shoegaze]

All Men Unto Me - Chemical Transit [classical/doom metal]

RXK Nephew - Till I'm Dead [rap/hiphop]

Panopticon - The Rime of Memory [rabm/black metal]

Yaeji - With A Hammer [electronic]

Row 8

DRAIN - LIVING PROOF [punk/hardcore]

909 Worldwide - Hardcore Will Never Die, and Neither Will You [happy hardcore/rave]

lobsterfight, gingerbee, Cicadahead, godfuck - a lobster, bee, & cicada walk into a bar and find god [skramz]

GingerBee - Our Skies Smile [skramz/5th wave emo]

Curta'n Wall - Siege Ubsessed! [dungeon synth/raw black metal]

GEZEBELLE GABURGABLY - Gaburger [alt pop]

crisis sigil - God Cum Poltergeist [cybergrind]

Row 9

Lamp Of Murmuur - Saturnian Bloodstorm [black metal]

Crystalline Thunderbolts - Blessed Hands Touch The Ophanim Under The Golden Rainbows [experimental black metal]

Tomb Mold - The Enduring Spirit [black/death metal]

FIRE TOOLZ - I am upset because I see something that is not there. [electro-industrial/experimental]

Angel Electronics - ULTRA PARADISE [happy post-hardcore]

Vylet Pony - Carousel (An Examination of the Shadow, Creekflow, and its Life as an Afterthought) [electronic]

Ada Rook - Rookie's Bustle [electronic]

This post took forever to make. Again if you have any thoughts on it please tell me!!!! And share widely with your friends :)

Love, Ary

[typhoidmeri]

‘neath the black, the sky looks dead

She’s walking on a ghost ship. Boots treading where the dead recently lay. Darcy’s hands are shaking, not in fear but from too many hours on her feet scrambling between Doctors Foster and Cho.

“I’m awake,” Darcy says to herself, frowning down at the tablet in her hand. She’s walking through the belly of the Ark Three scavenging whatever they can from the wreck of the ship like sea creatures harvesting a whale fall, back when whales swam and the sea teemed with life.

“Ensign Lewis, how much longer?”

Darcy scrunches up her nose, “I’ve found their genetic library and seed vault. I need a few minutes to cross reference their library with ours, Captain Rogers. Huh…that’s weird.” She looks from the Ark One tablet in hand to the Ark Three tablet in its plinth.

“Weird how?” Steve asks, looking over Darcy’s shoulder.

“You know, I was a cryo bay tech and this is below my pay grade. If we were getting paid and not on a spaceship built by a trillionaire headed for a planet that a space probe said looked shiny enough for a colony-“

“Lewis.”

“What I am trying to say in my official, unofficial designation as ship’s librarian/archivist/future experimental archeologist is that half of these species shouldn’t be here. On our ship, S.H.I.E.L.D. and SI sent us with the DNA of livestock animals: chickens, sheep, cows, and pigs, that sort of thing.”

“Right,” Steve nods. “We're a colony ship so it makes sense that we are sent with everything needed to establish a working colony. Like Parker’s vegetable crops keeping us alive.”

“Look,” Darcy says. She shuffled across the room, tablet forgotten as she read aloud, the neat little labels on the drawers, under her breath. “Here.”

“Apis mellifera,” Steve mouths. “You’re gonna have to help me here, I’m military, not science.”

“Honey bees,” Darcy says.

“Bees have been gone…I don’t know, a long time.”

“The Smithsonian had several species of bees in their genetic library. The same library that was supposedly destroyed in that terrorist attack. I’m guessing that a lot of these species were in that library too,” Darcy rocks on her heels, waving her hand at the glowing walls around them.

“You think Stark, the genius, that built the tin can, we are traveling the universe in, stole a library from the Smithsonian Institute?”

“I’m not, not saying it.”

The Captain, Acting Captain, pinches the bridge of his nose. “How much of this do you want to take?”

“All of it,” Darcy replies. She doesn’t need to think about it. Not really. The Ark Three is a crypt orbiting a dwarf exo planet. The only hope is with the Ark One, her crew, and their destination Proxima B.

“I really don’t want to be the one to say there is no room on Ark One.”

“You don’t have to,” Jane says, stepping into the storage library. There’s a smear of grease on her temple, and a tablet held on her open palm. “We’re going to take their cryo bay with us.”

Dr Foster, the Ark’s chief engineer, taps her tablet with a fingertip. A three-dimensional schematic of an Ark unfolds in the air above the screen. “The Ark bays are modular, built in pieces for easy assembly, alteration, and dissembling at Proxima B or wherever the other ships have been sent. We lost our cryo pod bays, but the connections, here and here,” Jane says, pointing out to places on the Ark with a finger. “…are intact, with a little work. One bay on this ship is untouched. I can bridge the gap. We can do this with your permission, Captain.”

“Acting Captain,” Rogers says. He folds his arms across his chest, brows furrowed, blue eyes staring through the Ark diagram. “Do it.”

…..

A little Syfy’s the Ark au. The show has it’s flaws, it’s very cheap at times, very incredibly cheap, and I want to burn the cheap not-a-smart-watch straps they are wearing. But the characters grow on you and it has potential, at least enough to inspire me to write this little scene.

While what I wrote isn’t wholly accurate to the plot of the show, it is how I would write it. If I could write more than a few paragraphs in as many months. Ah, well, it’s fun to play with the headcanon at least. Thanks for reading. I’m sorry I don’t have more to give you.

[jcmarchi]

10 Best AI Tools for Musicians (December 2024)

New Post has been published on https://thedigitalinsider.com/10-best-ai-tools-for-musicians-december-2024/

10 Best AI Tools for Musicians (December 2024)

Artificial intelligence is not just augmenting today’s music production – it is fundamentally reimagining how musicians create, practice, and interact with sound. From advanced stem separation to natural language synthesis, these tools represent the cutting edge of what is possible when neural networks meet musical creativity.

This collection of groundbreaking platforms showcases how AI is democratizing music production while pushing technical boundaries. Each tool brings unique innovations that are more than just incremental improvements on existing technology – they are radical reimaginings of what is possible in digital music creation.

Moises functions as an intelligent audio processing center where AI systems transform how musicians practice, create, and master their craft. The platform combines sophisticated audio separation technology with practical music education features, creating a comprehensive ecosystem for both aspiring and professional musicians across multiple platforms.

At its technical core, Moises operates through an advanced AI framework that processes complex audio signals in real-time. The system’s architecture enables simultaneous analysis of multiple audio components, separating intricate layers of music into distinct elements while maintaining exceptional sound quality. This foundation supports automated chord recognition systems that process musical patterns through sophisticated algorithms, creating accurate, synchronized chord progressions that adapt to different skill levels.

The platform’s Voice Studio represents an advanced implementation of AI voice modeling technology, processing vocal characteristics through neural networks to generate authentic voice transformations. This system connects with professional-grade recording equipment, enabling high-fidelity voice manipulation while maintaining natural-sounding results. The platform’s infrastructure extends to DAW integration through the Stems Plugin, creating a seamless bridge between AI-powered audio separation and professional music production workflows.

Key features

Multi-layer AI audio separation system with isolated instrument extraction

Neural network-powered chord detection with skill-level adaptation

Real-time pitch modification engine with key detection capabilities

Automated tempo analysis system with smart metronome integration

Multi-language lyrics transcription framework with automatic detection

Visit Moises→

Fadr combines advanced stem separation technology with intuitive production tools, making professional-quality music creation available to everyone through a web-based interface that keeps most of its capabilities free. The platform’s technical foundation centers on a sophisticated audio processing engine that breaks down complex musical arrangements into their core components. This system operates through parallel processing capabilities that simultaneously evaluate multiple audio layers, enabling precise extraction of individual instruments while maintaining pristine sound quality. The platform’s AI framework extends beyond basic audio separation, incorporating advanced pattern recognition technology that identifies musical elements like key signatures and chord progressions in real-time.

The integration of SynthGPT represents an innovative breakthrough in AI-powered sound design, processing complex audio parameters through neural networks to generate new musical elements. This architecture connects seamlessly with professional production environments through the Fadr Stems Plugin, enabling direct integration with major DAWs while maintaining consistent audio quality across different platforms.

Key features

Multi-instrument AI separation system with advanced component isolation

Real-time musical analysis engine with MIDI extraction capabilities

AI-powered remix creation framework with automatic synchronization

Live performance system with intelligent transition processing

Neural network sound generation through SynthGPT technology

Visit Fadr →

AIVA functions as an intelligent music composition studio where AI systems reinvent the creative process of soundtrack creation. The platform transforms complex musical composition into an accessible creative journey, enabling both novice enthusiasts and seasoned professionals to bring their musical visions to life through advanced AI technology.

The technical core of AIVA centers on sophisticated neural networks trained on vast collections of musical compositions. This system operates through intricate pattern recognition capabilities that understand the subtle nuances of different musical styles, from the dramatic swells of orchestral arrangements to the pulsing rhythms of electronic beats. The platform’s intelligence goes beyond basic composition, incorporating deep learning models that process user-provided influences to create unique musical fingerprints.

The system’s rapid composition engine is a breakthrough in creative AI technology, processing complex musical parameters through parallel computing architecture to generate complete pieces in seconds. This technical foundation enables seamless integration with various media formats while maintaining professional-grade audio quality, creating a unified ecosystem for soundtrack creation that bridges the gap between artificial and human creativity.

Key features

Neural network composition system supporting 250+ musical styles

Advanced influence processing engine for personalized creation

Real-time generation framework with rapid composition capabilities

Multi-format export architecture for universal compatibility

Flexible rights management system with varied ownership options

Visit AIVA →

SOUNDRAW is another AI platform for musicians that combines advanced compositional intelligence with intuitive controls, creating a streamlined environment where creators can generate professional-quality tracks without wrestling with technical complexities. The platform builds on sophisticated neural networks that process multiple musical parameters simultaneously. This system operates through an intricate web of algorithms that understand the subtle interplay between mood, genre, and musical structure, creating cohesive compositions that feel authentic and purposeful. The platform also incorporates deep learning models that maintain musical coherence while allowing precise control over individual elements.

The system’s API implementation enables scalable music creation, processing composition requests through high-performance computing architecture that delivers near-instantaneous results. This technical framework enables seamless integration with external applications while maintaining consistent quality across all generated tracks, creating a unified ecosystem for AI-powered music production that breaks down traditional barriers to creative expression.

Key features

Advanced AI composition engine with multi-parameter control

Real-time customization system with granular adjustment capabilities

Perpetual licensing framework with guaranteed rights clearance

Unlimited generation architecture supporting diverse project needs

API integration system with ultra-fast processing capabilities

Visit SOUNDRAW →

LANDR Studio functions as a comprehensive creative command center where AI systems transform raw musical potential into polished, professional productions. The platform unifies advanced mastering technology with extensive production resources, creating an integrated environment where artists can take their music from concept to streaming platforms while developing their craft.

The platform’s technical core centers on a sophisticated mastering engine that processes audio through neural networks trained on countless professional recordings. This system operates through intricate analysis algorithms that understand the subtle nuances of different genres and styles, crafting masters that enhance the natural character of each track. The intelligence extends beyond basic processing, incorporating deep learning models that make precise, contextual decisions about equalization, compression, and stereo imaging.

The platform’s collaborative framework assists in remote music production, processing high-quality video and audio streams while maintaining precise file synchronization. This connects seamlessly with an extensive resource ecosystem, including premium plugin architectures and a vast sample database, creating a unified creative space where technology enhances rather than complicates the artistic process.

Key features

Neural network mastering system with contextual audio processing

Multi-platform distribution framework reaching 150+ streaming services

Premium plugin integration architecture with 30+ professional tools

Sample management system hosting 2M+ curated sounds

Real-time collaboration engine with synchronized feedback capabilities

Visit LANDR →

Loudly combines advanced text-to-music capabilities with comprehensive customization tools. The platform’s technical foundation builds on an innovative dual-approach system that processes both text descriptions and musical parameters through AI. This enables a remarkable breakthrough in creative expression – the ability to translate written concepts directly into musical arrangements while maintaining precise control over technical elements.

The platform’s ethical framework leads in responsible AI music creation, processing compositions through a carefully curated dataset developed with artist consent. This helps ensure major distribution channels while maintaining strong copyright compliance, creating an ecosystem where technological innovation and artistic integrity coexist harmoniously. The result is a transformative tool that breaks down traditional barriers to music creation while respecting and protecting the broader musical community.

Key features

Advanced text-to-music conversion system with multi-parameter control

Dual-mode generation engine supporting both concept and parameter-based creation

Comprehensive stem separation architecture for detailed customization

Multi-platform distribution framework with major service integration

Ethical AI processing system with verified dataset compliance

Visit Loudly →

Playbeat functions as an intelligent rhythm laboratory where AI transforms the art of beat creation into an endless playground of possibilities. The platform reimagines traditional sequencing through an innovative approach to pattern generation, creating an environment where producers can break free from conventional rhythmic constraints while maintaining precise control over their music.

Playbeat uses a sophisticated multi-engine system that processes rhythm through eight independent neural pathways. This breakthrough in beat generation operates through parallel processing capabilities that simultaneously evaluate multiple parameters – from subtle pitch variations to intricate density patterns. The system also incorporates smart algorithms that ensure each new pattern feels both fresh and musically coherent, while never exactly repeating itself. The platform’s real-time manipulation framework processes parameter adjustments with zero latency while maintaining synchronization. This can be used with both internal and external sound sources, creating a unified environment for rhythm experimentation.

Key features

Multi-engine sequencer system with independent parameter control

Smart randomization architecture ensuring unique pattern generation

Flexible sample management framework with custom import capabilities

Real-time processing engine for dynamic parameter manipulation

Cross-platform export system supporting multiple formats

Visit Playbeat →

Image: Magenta Studio

Magenta is an innovative creative laboratory representing Google Brain’s vision of open collaboration, creating an environment where developers, artists, and researchers can explore AI-driven creativity through accessible, powerful tools. Magenta centers on a sophisticated suite of neural networks built upon TensorFlow’s robust architecture. This system operates through multiple learning paradigms, from deep learning models that understand the subtle patterns of musical composition to reinforcement learning algorithms that explore new creative possibilities. The platform’s breakthrough NSynth technology is a fundamental reimagining of sound synthesis, processing complex audio characteristics through neural networks to create entirely new possibilities.

The Magenta Studio implementation marked a significant advancement in accessible AI music creation, processing complex musical algorithms through an intuitive interface that connects directly with professional production environments. This enables artists to explore new creative territories while maintaining precise control over their artistic vision. The platform’s open-source nature ensures that these innovations remain transparent and collaborative, fostering a community-driven approach to advancing AI creativity.

Key features

Advanced neural network architecture built on TensorFlow

DAW integration framework through Magenta Studio

Neural synthesis engine for innovative sound creation

Open collaboration system with comprehensive documentation

Multi-modal generation capabilities across various creative domains

Visit Magenta →

LALAL.AI functions as an audio manipulation platform where advanced AI brings high accuracy to stem separation and audio enhancement, creating a powerful environment where complex audio signals can be deconstructed and refined with precision. The technical heart of LALAL.AI beats through sophisticated neural networks specifically engineered for audio signal analysis. This system understands the subtle interplay between different sonic elements, from the breathy nuances of vocals to the complex harmonics of orchestral instruments.

The platform also incorporates advanced noise reduction algorithms that can identify and remove unwanted artifacts while preserving the natural character of the source material. The platform’s desktop implementation enables the processing of complex audio operations through a local architecture that delivers professional-grade results without internet dependency. This enables seamless batch processing while maintaining consistent quality across all operations.

Key features

Multi-stem separation system with 10-component isolation capabilities

Advanced noise reduction engine with adjustable processing controls

Echo elimination framework with precise reverb extraction

Vocal isolation architecture with dual-stream processing

Local processing system supporting batch operations

Visit LALAL →

Dreamtonics is a vocal synthesis tool that combines cutting-edge AI technology with intuitive creative tools. The platform can process the intricate nuances of human singing – from subtle vibrato variations to complex emotional inflections. Its cross-lingual capabilities showcase an extraordinary advancement in voice synthesis, enabling voices to move seamlessly across language boundaries while maintaining natural expressiveness and cultural authenticity.

The tool’s Vocoflex technology is a significant step forward in real-time voice transformation, processing vocal characteristics through dynamic neural engines that enable immediate modification and experimentation. The framework connects with professional audio production environments through VST3 and AudioUnit integration, creating a unified ecosystem for vocal creation. Each voice database adds a new dimension to this creative palette, with different characters representing distinct nodes in an expanding network of vocal possibilities.

Key features

Neural network synthesis engine with multi-language capabilities

Real-time transformation system for live vocal processing

Cross-lingual framework supporting multiple language bases

Professional DAW integration architecture

Extensive voice database system with unique character profiles

Visit Dreamtonics →

The Future of AI in Music Creation

As we have explored these innovative platforms, a clear picture emerges of AI’s transformative impact on music creation. We are moving beyond simple automation into an era where artificial intelligence becomes a true creative collaborator. These tools do not just make music production easier – they open up entirely new possibilities for creative expression.

What is particularly exciting is how these platforms complement rather than replace human creativity. Whether it’s Dreamtonics’ breakthrough in vocal synthesis or Magenta’s open-source exploration of creative AI, each tool augments human capabilities while maintaining the essential human element that makes music meaningful.

As neural networks become more sophisticated and processing power continues to advance, we can expect even more groundbreaking innovations in this space. The future of music creation lies not in choosing between human and artificial intelligence, but in the powerful synthesis of both – where AI handles complex technical challenges while humans focus on creative vision. This symbiotic relationship promises to make music creation more accessible, more innovative, and more exciting than ever before.

[splatoonna]

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[techfinna]

Top 5 Selling Odoo Modules.

In the dynamic world of business, having the right tools can make all the difference. For Odoo users, certain modules stand out for their ability to enhance data management and operations. To optimize your Odoo implementation and leverage its full potential. 

That's where Odoo ERP can be a life savior for your business. This comprehensive solution integrates various functions into one centralized platform, tailor-made for the digital economy. 

Let’s drive into 5 top selling module that can revolutionize your Odoo experience:

Dashboard Ninja with AI, Odoo Power BI connector, Looker studio connector, Google sheets connector, and Odoo data model.

1. Dashboard Ninja with AI: 

Using this module, Create amazing reports with the powerful and smart Odoo Dashboard ninja app for Odoo. See your business from a 360-degree angle with an interactive, and beautiful dashboard.

Some Key Features:

Real-time streaming Dashboard

Advanced data filter

Create charts from Excel and CSV file

Fluid and flexible layout

Download Dashboards items

This module gives you AI suggestions for improving your operational efficiencies.

2. Odoo Power BI Connector:

This module provides a direct connection between Odoo and Power BI Desktop, a Powerful data visualization tool.

Some Key features:

Secure token-based connection.

Proper schema and data type handling.

Fetch custom tables from Odoo.

Real-time data updates.

With Power BI, you can make informed decisions based on real-time data analysis and visualization.

3. Odoo Data Model: 

The Odoo Data Model is the backbone of the entire system. It defines how your data is stored, structured, and related within the application.

Key Features:

Relations & fields: Developers can easily find relations ( one-to-many, many-to-many and many-to-one) and defining fields (columns) between data tables. 

Object Relational mapping: Odoo ORM allows developers to define models (classes) that map to database tables.

The module allows you to use SQL query extensions and download data in Excel  Sheets.

4. Google Sheet Connector:

This connector bridges the gap between Odoo and Google Sheets.

Some Key features:

Real-time data synchronization and transfer between Odoo and Spreadsheet.

One-time setup, No need to wrestle with API’s.

Transfer multiple tables swiftly.

Helped your team’s workflow by making Odoo data accessible in a sheet format.

5.  Odoo Looker Studio Connector:

Looker studio connector by Techfinna easily integrates Odoo data with Looker, a powerful data analytics and visualization platform.

Some Key Features:

Directly integrate Odoo data to Looker Studio with just a few clicks.

The connector automatically retrieves and maps Odoo table schemas in their native data types.

Manual and scheduled data refresh.

Execute custom SQL queries for selective data fetching.

The Module helped you build detailed reports, and provide deeper business intelligence.

 These  Modules will improve analytics, customization, and reporting. Module setup can significantly enhance your operational efficiency. Let’s embrace these modules and take your Odoo experience to the next level. 

Need Help?

I hope you find the blog helpful. Please share your feedback and suggestions.

For flawless Odoo Connectors, implementation, and services contact us at 

info@techfinna.com Or www.techneith.com  

[sohojware]

Which Payment Gateways Are Compatible for Dynamic Websites - A Comprehensive Guide by Sohojware

The digital landscape is constantly evolving, and for businesses with dynamic websites, staying ahead of the curve is crucial. A dynamic website is one that generates content on the fly based on user input or other factors. This can include things like e-commerce stores with shopping carts, membership sites with customized content, or even online appointment booking systems.

For these dynamic websites, choosing the right payment gateway is essential. A payment gateway acts as a secure bridge between your website and the financial institutions that process payments. It ensures a smooth and safe transaction experience for both you and your customers. But with a plethora of payment gateways available, selecting the most compatible one for your dynamic website can be overwhelming.

This comprehensive guide by Sohojware, a leading web development company, will equip you with the knowledge to make an informed decision. We’ll delve into the factors to consider when choosing a payment gateway for your dynamic website, explore popular options compatible with dynamic sites, and address frequently asked questions.

Factors to Consider When Choosing a Payment Gateway for Dynamic Websites

When selecting a payment gateway for your dynamic website in the United States, consider these key factors:

Security: This is paramount. The payment gateway should adhere to stringent security protocols like PCI DSS compliance to safeguard sensitive customer information. Sohojware prioritizes security in all its development projects, and a secure payment gateway is a non-negotiable aspect.

Transaction Fees: Payment gateways typically charge transaction fees, which can vary depending on the service provider and the type of transaction. Be sure to compare fees associated with different gateways before making your choice.

Recurring Billing Support: If your website offers subscriptions or memberships, ensure the payment gateway supports recurring billing functionalities. This allows for automatic and convenient payment collection for your recurring services.

Payment Methods Supported: Offer a variety of payment methods that your target audience in the US is accustomed to using. This may include credit cards, debit cards, popular e-wallets like PayPal or Apple Pay, and potentially even ACH bank transfers.

Integration Complexity: The ease of integrating the payment gateway with your dynamic website is crucial. Look for gateways that offer user-friendly APIs and clear documentation to simplify the integration process.

Customer Support: Reliable customer support is vital in case you encounter any issues with the payment gateway. Opt for a provider with responsive and knowledgeable customer service representatives.

Popular Payment Gateways Compatible with Dynamic Websites

Here’s a glimpse into some of the most popular payment gateways compatible with dynamic website:

Stripe: A popular and versatile option, Stripe offers a robust suite of features for dynamic websites, including recurring billing support, a user-friendly developer interface, and integrations with various shopping carts and platforms.

PayPal: A widely recognized brand, PayPal allows customers to pay using their existing PayPal accounts, offering a familiar and convenient checkout experience. Sohojware can integrate PayPal seamlessly into your dynamic website.

Authorize.Net: A secure and reliable gateway, Authorize.Net provides a comprehensive solution for e-commerce businesses. It supports various payment methods, recurring billing, and integrates with popular shopping carts.

Braintree: Owned by PayPal, Braintree is another popular choice for dynamic websites. It offers a user-friendly API and integrates well with mobile wallets and other popular payment solutions.

2Checkout (2CO): A global payment gateway solution, 2Checkout caters to businesses of all sizes. It offers fraud prevention tools, subscription management features, and support for multiple currencies.

Sohojware: Your Trusted Partner for Dynamic Website Development and Payment Gateway Integration

Sohojware possesses extensive experience in developing dynamic websites and integrating them with various payment gateways. Our team of skilled developers can help you choose the most suitable payment gateway for your specific needs and ensure a seamless integration process. We prioritize user experience and security, ensuring your customers have a smooth and secure checkout experience.

1. What are the additional costs associated with using a payment gateway?

Besides transaction fees, some payment gateways may charge monthly subscription fees or setup costs. Sohojware can help you navigate these costs and choose a gateway that fits your budget.

2. How can Sohojware ensure the security of my payment gateway integration?

Sohojware follows best practices for secure development and adheres to industry standards when integrating payment gateways. We stay updated on the latest security protocols to safeguard your customer’s financial information.

3. Does Sohojware offer support after the payment gateway is integrated?

Yes, Sohojware provides ongoing support to ensure your payment gateway functions smoothly. Our team can address any issues that arise, troubleshoot problems, and provide updates on the latest payment gateway trends.

4. Can Sohojware help me choose the best payment gateway for my specific business needs?

Absolutely! Sohojware’s experts can assess your business requirements, analyze your target audience, and recommend the most suitable payment gateway based on factors like transaction volume, industry regulations, and preferred payment methods.

5. How long does it typically take to integrate a payment gateway with a dynamic website?

The integration timeline can vary depending on the complexity of the website and the chosen payment gateway. However, Sohojware’s experienced team strives to complete the integration process efficiently while maintaining high-quality standards.

Conclusion

Choosing the right payment gateway for your dynamic website is crucial for ensuring a seamless and secure online transaction experience. By considering factors like security, fees, supported payment methods, and integration complexity, you can select a gateway that aligns with your business needs. Sohojware, with its expertise in web development and payment gateway integration, can be your trusted partner in this process. Contact us today to discuss your requirements and get started on your dynamic website project.