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Elevating the Cosmos SDK: Eden (v0.50)

November 08, 2023

Eden (v0.50) marks a significant stride in the evolution of the Cosmos SDK, ushering in an era of enhanced modularity and performance.

This release is a powerful confluence of pivotal features, such as the completion of ABCI++, which revolutionizes consensus flexibility, and the introduction of AutoCLI, reducing the complexity of module creation, enabling a smoother and more efficient developer experience.

IAVL 1.0 is another highlight, bringing forth substantial improvements in disk storage and overall performance, representing a critical advancement in the Cosmos SDK infrastructure. New additions like Optimistic Execution further enhance performance, enabling the execution of block time ahead of consensus.

Also notable is the introduction of Sign Mode Textual, improving transaction signing experience, ensuring that the Cosmos SDK continues to stay at the forefront of technological innovation.

Below are some other key and notable changes in the Eden release. You can view the full changelog here.

ABCI++

With this release, CometBFT and Cosmos SDK teams will have delivered the final piece of ABCI++: Vote Extensions. In the previous release, we delivered the preliminary phases of ABCI++: Prepare and Process Proposal. Vote Extensions complete this process by allowing users to add arbitrary data to consensus. Some example use cases include leveraging vote extensions for oracles instead of having validators submit transactions to be included in a block. It also can be applied in conjunction with MEV use cases, addressing front-running issues or implementing Threshold Decryption.

Coinciding with the integration of ABCI++, other changes were made to the core layer including:

BeginBlock and Endblock are private and Finalizeblock is exposed for testing purposes. This allows modules to test in the same way as before.
When upgrading a chain, Preblock is meant to run before BeginBlock. This will upgrade the chain and migrate state. This was meant as a separation of concerns for upgrades to not conflate it with the normal operations of a chain.

AutoCLI

At the beginning of 2023, we set out to reduce the total lines of code needed to create a module. CLI is optional, but many users do implement it as CLI is used heavily by countless users.

With this in mind, we are pleased to announce the release of AutoCLI in Cosmos SDK Eden.

CLI for your custom modules can now be automatically generated when needed, reducing the amount of boilerplate when writing a module. If you’d like to learn more, the documentation can be found here.

IAVL 1.0

In conjunction with this release, the rollout of IAVL 1.0 has facilitated significant improvements to disk storage and overall performance, with a fundamental change in the key structure of the IAVL tree. In the earlier versions of IAVL, the original key format was a hash key meaning when the dataset grew, accessing the data became slower and less efficient.

The new key structure combines the version and local nonce within the key. The reason that this change improves data locality is because it reduces the randomness in how data is stored on disk. By including the version in the key, nodes are grouped together based on the version in which they were added to the tree, meaning that nodes from a particular version are stored together on disk. Similarly, the local nonce is used to distinguish nodes that belong to the same version but have different positions within that version allowing the tree to keep a specific order for nodes within the same version, again allowing retrieval to be more structured and organised.

Modules

Writing a module has been notoriously verbose in the past, with this release we have made it simpler with things like Collections and ORM. These packages are abstractions over the state layer providing powerful yet simple ways of interacting with state. In the future these packages will export the on-disk schema to be used with query layers, like GraphQL, to build complex and scalable clients. Additional changes include:

Gov added expedited proposals and stay tuned for multiple choice proposals and optimistic voting coming the next release.
Capability module was moved to the Cosmos SDK module.
BeginBlock signature does not import CometBFT types and returns an error
Endblock does not import CometBFT unless there is a need to return a validator set. This should be handled by the staking module, leaving all chains to not worry about this.
All modules have been migrated to use collections
Validate Basic is no longer required and validation of the method can be performed directly in the message server.

GetSignBytes which was part of sdk.Msg is no longer required. It is automatically inferred based on the protobut message. If you have existing modules and would like to keep the same encoding in Amino, you will need to annotate some of the fields in your proto messages. Documentation on the fields can be found here. In order to verify your encoding is the same we recommend creating a golden test.

Optimistic Execution

As previously stated, we are actively towards enhancing performance further and one of the features contributing to this is optimistic execution.

Optimistic Execution is the ability to be able to execute block time ahead of time, having the response ready prior to the consensus layer requesting it. It is Optimistic as it can still be rejected although we are executing the block.

In load tests on Sei Network, Optimistic Execution cut the block time by 50%. On fast block-producing networks like Injective, OE can help the network generate sub-1-second blocks without any extra effort.

To read more on Optimistic Execution, see our blog post here.

Sign Mode Textual

Eden also introduces a new sign mode, sign_mode_textual, which improves the signing of transactions with new hardware devices. The aim is to produce a more human-readable way to approve, review and view transaction details. Currently, this is only available on Ledger devices.

More information can be found here.

Funded by the Interchain Foundation, the development of the Cosmos SDK is led by Binary Builders. For their work and feedback on the Eden release, we would like to thank the following teams: Zondax, Orijtech, Crypto.com, Osmosis and Berachain.

We look forward to hearing your feedback and suggestions on how to further improve the Cosmos SDK.

About the Cosmos SDK

Cosmos SDK is the world’s most popular framework for building application-specific blockchains.

Written in Go, Cosmos SDK allows developers to quickly and easily build and deploy application-specific blockchains, reducing time to market, using a framework that is stable, secure and highly performant.

Cosmos SDK is the native application-building toolkit at the core of the interchain.

Learn more about Cosmos SDK or browse the docs here.

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🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖

The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.

Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.

Now that AI is moving into the physical world, many are asking a bigger question:

Will these same companies end up controlling robotics too?

It's a valid concern.

The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.

But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.

That movement is decentralized AI.

Why Decentralized AI Exists

The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.

This means anyone can contribute compute, contribute models, validate outputs and can participate.

The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.

Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.

Why This Matters for Robotics

Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.

The challenge is that collecting and training on real-world robotic data is incredibly expensive. That's one reason large companies have such an advantage. They can afford the compute, simulation environments, and data infrastructure needed to train robotics models at scale.

This is where decentralized systems become interesting.

Instead of one company collecting all the data and training all the models, decentralized networks could allow thousands of contributors to participate in building robotic intelligence.

Imagine a future where:

Warehouse robots contribute operational data.
Delivery robots contribute navigation data.
Factory robots contribute manipulation data.
Developers contribute models.
Validators evaluate performance.

The resulting intelligence becomes a shared network rather than a proprietary asset.

That vision is beginning to emerge.

Bittensor's Move Toward Physical AI

While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.

One example is Kinitro, a subnet focused on incentivizing the training and evaluation of embodied AI systems. The goal is to create competitive environments where developers build robotic intelligence and are rewarded based on performance.

The broader Bittensor ecosystem has also expanded into compute marketplaces, distributed inference systems, bandwidth infrastructure, and AI coordination layers that could eventually support robotics workloads. Several subnets now focus on decentralized compute, confidential inference, data transfer, and model training, critical components for future robotic systems.

In other words, the pieces are starting to appear.

Not a decentralized robot network yet.

But the infrastructure that could support one.

Beyond Bittensor: The Rise of Physical AI Networks

Bittensor isn't alone.

Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.

New research published in 2026 introduced the concept of DAO-enabled decentralized physical AI, or DePAI. The idea combines robotics, decentralized infrastructure, AI models, governance systems, and human oversight into a single framework. Instead of centralized control, robots and physical infrastructure could be coordinated through transparent rules and distributed ownership models.

At the same time, developers are exploring decentralized operating systems for robots that allow machines to communicate directly with each other and with distributed compute resources. These architectures are designed to make robotic systems more resilient and less dependent on a single cloud provider.

The goal is not simply decentralization for its own sake.

The goal is resilience.

If one server fails, the system continues.

If one company disappears, the network survives.

If one participant leaves, innovation continues.

But Here's the Reality

Decentralized AI faces the same challenge every decentralized technology faces.

Big Tech has resources. A lot of resources.

Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.

That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.

And there are legitimate concerns about whether decentralized networks can maintain quality, reliability, and security at the scale required for industrial robotics. Even researchers studying decentralized AI systems have highlighted risks around concentration, incentives, governance, and network security.

The challenge isn't just decentralizing intelligence.

It's decentralizing intelligence while maintaining performance.

That's much harder.

The Most Likely Outcome

The future probably won't be fully centralized. And it probably won't be fully decentralized either. Instead, we're likely heading toward a hybrid model.

Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.

At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.

The companies building robots may use NVIDIA hardware.

Train on Azure.

Run foundation models from OpenAI.

But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.

The future of robotics could end up looking less like a monopoly and more like an ecosystem.

The Bigger Question

The real question isn't whether decentralized AI can eliminate Big Tech.

It can't.

At least not anytime soon.

The real question is whether decentralized AI can prevent a future where a handful of companies control every robot, every model, every dataset, and every decision made by the machines operating around us.

As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.

Because the battle for the future of robotics is no longer about hardware.

It's about who owns the intelligence.

And that battle is just getting started.

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Dinarian888@dinarian888

August 09, 2026

Navigating the world of blockchain 🧭

Navigating the world of blockchain can feel like learning a completely foreign language. Between technical jargon and fast-moving Web3 terminology, getting started can be overwhelming.

Whether you are exploring digital assets, building on-chain, or simply trying to understand decentralized technology, here is your foundational glossary of essential blockchain terms every beginner should know.

🏛️ 1. Core Architecture: The Base Layer

Blockchain: A distributed, immutable digital ledger that records transactions across a peer-to-peer network of computers. Once data is written to a block and added to the chain, it cannot be altered without altering all subsequent blocks.
Block: A collection of verified transactions grouped together. Once filled, the block is cryptographically linked to the previous one, forming a chronological "chain."
Node: An individual computer connected to a blockchain network that helps validate transactions, store ledger data, and maintain network consensus.
Consensus Mechanism: The set of rules and algorithms that network nodes use to agree on the validity of transactions.
- Proof of Work (PoW): Requires miners to solve complex mathematical puzzles using computational power (e.g., Bitcoin).
- Proof of Stake (PoS): Requires validators to lock up ("stake") native tokens as collateral to participate in block validation (e.g., Ethereum).

🔑 2. Ownership & Security: Wallets and Keys

Public Key (Address): An alphanumeric string that acts like your bank account number or email address. It is safe to share publicly so others can send you digital assets.
Private Key: A secret cryptographic passphrase or key that grants full access and control over your wallet assets. Never share your private key or seed phrase with anyone.
Seed Phrase (Recovery Phrase): A sequence of 12 to 24 random words generated when you set up a wallet. It acts as the master backup key to restore your wallet and access your funds on any device.
Hot Wallet vs. Cold Wallet:
- Hot Wallet: A software-based crypto wallet connected to the internet (e.g., browser extensions, mobile apps), making it convenient for frequent transactions but higher risk.
- Cold Wallet: An offline hardware device (e.g., Ledger, Coldcard) designed to isolate private keys from internet-connected threats.

⚙️ 3. Execution & Functionality: Smart Contracts and Apps

Smart Contract: Self-executing code stored on a blockchain that automatically enforces agreement terms once predetermined conditions are met—eliminating the need for intermediaries.
dApp (Decentralized Application): Applications built on top of a blockchain network that run via smart contracts rather than centralized cloud servers.
Gas Fees: Network transaction fees paid to validators or miners to cover the computational energy required to process actions on a blockchain.
Layer 1 vs. Layer 2:
- Layer 1 (L1): The underlying primary blockchain network (e.g., Bitcoin, Ethereum, Solana) that handles base security and finality.
- Layer 2 (L2): Secondary frameworks or companion networks built on top of an L1 to increase transaction speeds and lower gas fees (e.g., Arbitrum, Optimism, Base).

💰 4. Financial & Market Concepts

Tokenomics: The economic design, supply dynamics, utility, and distribution model of a cryptocurrency or token project.
DeFi (Decentralized Finance): Financial services—such as lending, borrowing, trading, and earning interest—built on smart contracts without traditional banks or financial intermediaries.
Liquidity: The ease with which an asset can be bought or sold in a market without significantly impacting its price.
DYOR (Do Your Own Research): A foundational golden rule in the Web3 space reminding users to independently verify technical code, whitepapers, and team backgrounds before making any capital commitments.

💡 Quick Cheat Sheet

"Not your keys, not your coins."

If you do not hold the private keys or seed phrase to your digital wallet, you do not truly own the assets inside it—a centralized entity or exchange does. Always prioritize security first as you explore the space.

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Dinarian888@dinarian888

July 18, 2026

AI Is Coming for Your Job Title

Artificial intelligence may or may not take your job, but it has already broken into the human resources department and vandalized the org chart.

The evidence is all over LinkedIn, where perfectly serviceable occupations now arrive wearing titles such as “forward-deployed and agentic AI architect.” That person may be building sophisticated software. They may also be helping a chatbot remember what happened three prompts ago. Either way, somebody approved the business cards.

The expanding AI lexicon offers a useful counterpoint to the darker debate about technology and employment. Most discussion centers on how many jobs AI will eliminate. Hiring data presents a more complicated picture that includes a weak overall labor market containing a small but rapidly growing neighborhood of AI-related work.

Indeed Hiring Lab found that the number of postings on Indeed mentioning AI surged 134% from its February 2020 level by the end of 2025, even as total postings stood only 6% above that benchmark. AI appeared in a record 4.2% of Indeed postings in December.

AI, in other words, is not merely changing work. It is adding syllables to it.

The Titles Employers Actually Want

The undisputed champion is AI engineer, which ranked No. 1 on LinkedIn’s 2026 Jobs on the Rise list. The ranking, based on growth during the previous three years, also highlighted AI consultants and strategists, AI and machine-learning researchers and data annotators.

The title is popular partly because it is wonderfully accommodating. An AI engineer might build applications around large language models, connect corporate data to an AI system, improve model performance or spend Thursday afternoon persuading a customer service bot not to offer refunds for products the company doesn’t sell.

Indeed’s data showed the terminology spreading beyond Silicon Valley. Nearly 45% of data and analytics postings contained an AI-related term at the end of 2025, along with roughly 15% of marketing postings and 9% of human resources listings. A more recent Indeed analysis reported by Business Insider found that the number of frequently advertised job titles explicitly referencing AI rose from 264 in 2022 to 822 in the first quarter of 2026. Nearly two-thirds were outside traditional technology fields.

That produces titles such as AI marketing manager, AI learning specialist, responsible AI counsel and AI transformation lead. These are not always new occupations. Frequently, they are familiar jobs that have discovered a highly effective résumé keyword.

LinkedIn data cited by the World Economic Forum estimated that AI investment has supported 1.3 million positions, including AI engineers, data annotators and forward-deployed engineers, plus more than 600,000 AI-enabled data center jobs. The server racks, unlike the chatbots, still need electricians.

The Jobs With the Science-Fiction Salaries

At the upper end, AI has created a compensation market that resembles professional sports, except the competitors wear hoodies and discuss inference latency.

A Syracuse University review put chief AI officer compensation between $200,000 and more than $500,000, while specialized roles can exceed $400,000 after bonuses and equity. Frontier research engineers, AI infrastructure specialists and engineers who can train or deploy advanced models command some of the largest packages.

Then there is the forward-deployed engineer, an old Palantir title that the AI boom has placed on a rocket sled. These engineers embed with customers, translating an executive’s desire to “do something with AI” into software that works. The Next Web reported that Indeed postings for the role were about 19 times higher in January than a year earlier.

A CTO guide from the blog Signal Through the Noise placed forward-deployed engineer compensation between $238,000 and $700,000, research-engineering packages as high as $1.4 million and chief AI officer compensation above $1 million in some cases. It also made a less flattering observation: Many lavishly differentiated titles describe the same three basic functions. People build AI products, train models or keep the infrastructure from catching fire.

The Department of Unnecessary Titles

AI has created some genuinely new work. Evals engineers design tests to determine whether models perform reliably. AI red teamers try to make systems fail before customers do. Model behavior engineers study why an AI system responds as it does. AI governance leaders manage risks involving data, bias, security and regulation.

Other titles seem to have escaped from a brainstorming retreat.

There is the Claude Evangelist, whose mission apparently combines product education with the traditional duties of an apostle. There are vibe coders, who build software by describing what they want and accepting AI-generated code with varying degrees of supervision. “Vibe engineer” is the more respectable version, roughly equivalent to putting on a blazer before asking the machine to fix the login page.

“Context engineer” is a real discipline involving the data, instructions, memory and tools supplied to AI models. “Prompt engineer,” once advertised as a possible six-figure profession for gifted chatbot whisperers, is increasingly treated as one skill inside a broader AI role.

The CTO guide also identified “builder,” “AI-native developer,” “RAG engineer,” “agentic AI engineer” and “principal agentic GenAI forward-deployed context architect,” the last of which appears to require both technical proficiency and exceptional lung capacity.

Has AI created entirely new jobs? Absolutely. Some occupations, including AI safety, evaluation and model governance, exist because modern generative systems introduced new technical and business problems. However, many job titles are old jobs with fresh vocabulary, higher salary bands and a sudden aversion to the words “software developer.”

That may be the safest prediction about AI and employment. The machines will automate some tasks, generate others and force companies to rethink the division of labor. Before any of that is settled, however, corporate America will form a steering committee, appoint a chief agentic transformation evangelist and schedule a meeting to determine what that person does.

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