2026-04-07 17:52:48
Ontario, Canada, April 6th, 2026, Chainwire
CoinRabbit Cuts Crypto Lending Rates
CoinRabbit has lowered crypto lending rates, which now start at 11.95%.
The platform offers a range of liquidation LTV options, from a standard market setup at 80% to a more conservative risk management approach at 90–95%.
This is one of the most competitive offers in the CeFi lending space, in terms of interest rates and loan terms.
CoinRabbit announces a reduction in crypto lending rates across XRP loans and more than 300 other assets, showing its dedication to offering practical tools for capital preservation. With prices fluctuating sharply, selling holdings can lock in losses and reduce future upside, while borrowing against crypto allows users to maintain portfolio exposure and access liquidity at the same time.
Historically, CoinRabbit APR reflected prevailing market conditions, ranging from 17%. Today, rates start at 11.95%, with participants in CoinRabbit’s Private Program able to access lower custom rates tailored to borrowing needs. Final rates are determined by the LTV ratio (50–90%) and loan terms, with options for both fixed-term and open-ended loans.
“Reducing rates is part of refining the financial model to make lending more efficient for diverse portfolios. In today’s dynamic market, the goal is to provide a capital preservation tool that offers liquidity while keeping assets invested,” said Walter Barrett, Chief Strategy & Growth Officer at CoinRabbit.
A key aspect of risk management in lending is the liquidation LTV: the ratio of the loan amount to the collateral value at which a loan is liquidated. On the market, the standard liquidation LTV ranges from 78% to 83%, meaning positions are liquidated once the collateral drops to that level.
CoinRabbit provides two options: a standard 80% liquidation LTV, and a 90–95% liquidation LTV for users seeking additional flexibility, as liquidation occurs later, giving a larger buffer for price drops. Let’s take a closer look at both options.
For example, an investor pledges $10,000 worth of XRP as collateral with the 90–95% liquidation LTV option. If they borrow $5,000 (loan amount), the initial loan-to-value (LTV) ratio is 50%. The position could be liquidated if the collateral value falls to $5,500, corresponding to a liquidation LTV of 90%. Instant alerts are sent as the collateral approaches this threshold, giving borrowers time to adjust their positions.
For users with some experience in crypto lending, the 80% liquidation LTV option represents the standard across most platforms. Using the same example, if an investor pledges $10,000 worth of XRP and borrows $5,000, the position would be at risk of liquidation once the collateral value falls to $6,250. Alerts are similarly sent as the collateral approaches this level, allowing borrowers to manage positions.
The choice ultimately depends on the user’s experience and preference for following the standard path (liquidation LTV 80%) or opting for a more conservative risk approach (liquidation LTV 90–95%).
How Lowered Crypto Lending Rates Work on CoinRabbit
CoinRabbit is a crypto asset management platform designed for long-term capital preservation. It enables flexible liquidity management through instant payments, lending, yield, trading products, and the Private Program — all within a single ecosystem. Since 2020, CoinRabbit has issued over $1.45B in loans, maintaining a 100% capital reserve to keep clients’ funds secure and never reused.
Services provided in Canada are offered by 1001285225 ONTARIO INC. For more information, users can visit the CoinRabbit website.
For media inquiries, users can contact: [email protected]
CMO
Irene Afanaseva
CoinRabbit
:::tip This story was published as a press release by Chainwire under HackerNoon’s Business Blogging Program
:::
Disclaimer:
This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR
2026-04-07 17:19:36
As companies grow, they can expand their offerings, reach new customers and markets, enhance profitability, and position themselves ahead of competitors. Whether trying to expand their company size by adding new departments or increase revenue by improving productivity per employee, companies can achieve growth through various strategies.
One of the strongest company growth strategies is retaining skilled employees who drive productivity, innovation, morale, and customer satisfaction.
Employee retention is a company’s ability to maintain a stable workforce by keeping employees for longer. High retention rates usually reflect a positive work environment and company culture, as employees feel valued and motivated to stay rather than seek job opportunities elsewhere.
Here is why employee retention is essential for company growth in 2026:
Retention costs less than acquisition: The average cost of replacing an employee is over £30,000 in the UK. Strong retention means more money can be invested in innovation and technology, rather than in recruitment and onboarding.
Retention boosts productivity: Long-tenured employees work faster and make fewer mistakes because they already understand systems, customers, and internal processes. This consistency keeps output high, instead of productivity resetting every time a team member leaves.
Retention drives revenue: Experienced employees have already built healthy customer or client relationships, which directly increases revenue per employee.
This article explores four employee retention strategies to keep in mind as you strive for company growth in 2026.
\
It’s essential to enhance the onboarding experience so employees feel welcome and ready to work from the get-go, as 80% of employees plan to quit their jobs after feeling undertrained due to poor onboarding.
The best way for human resource (HR) professionals to enhance the onboarding experience is to simplify the process, introduce new talent to teams and departments, discuss career development opportunities, and assign them a mentor or buddy.
Companies should offer clear development opportunities to all employees. By offering training, coaching, mentoring, reskilling, and upskilling, you are not only investing in their growth but also in the company's growth.
Employees who have been reskilled with specific job positions and requirements in mind are ready to take on new roles within your company as you grow, which boosts retention. For example, allowing employees to complete artificial intelligence (AI) and automation training ahead of adopting the technology.
Recognising and rewarding employees fairly can make them feel valued, seen, and appreciated. It can also affect engagement and productivity, as employees work hard to meet goals and reach milestones. Both of these benefits increase retention.
Here are some ways you can recognise or reward your employees:
Flexible working arrangements, such as remote and hybrid roles, flexible hours, and compressed workweeks, help employees maintain a healthy work-life balance. This enables them to manage childcare and pets better, stick to their hobbies, keep on top of their health and wellbeing, and avoid long and costly commutes.
\ A recent study revealed that only 35% of UK workers believe their company’s current flexible working policy is fit for purpose. This proves that companies that want to retain employees need to welcome flexible working arrangements before top talent leaves for better opportunities elsewhere.
\
For companies looking to grow in 2026, retention is one of the strongest strategies available.
Companies that invest as much in their employees as they do in technology and expansion will successfully outgrow their competitors.
By prioritising engaging onboarding experiences, development opportunities, meaningful recognition, and flexible working arrangements, companies can build stable teams that drive long-term success.
2026-04-07 16:14:59
daVinci-MagiHuman is a 15B unified model that generates synchronized video and speech from text prompts with fast, high-quality results.
2026-04-07 14:30:05
I’ve been running a long-term experiment: can a single person, using neural networks, develop and maintain a massive codebase that matches banking solutions in reliability, while shipping features at the speed of a pet project? These are, of course, hardly compatible things, but my goal is to at least walk the path of a startup with 0 employees and tight deadlines.
Currently, I'm working on a pet project, called Cynosure — an AI runtime in Golang aimed at running agents in the cloud, at scale, for a large number of users. Architecturally, it’s a highly complex project, and the speed of prototyping often harshly conflicts with code hygiene. It was initially designed following Domain-Driven Design (DDD) principles. It’s a complex architecture from a design standpoint, and the implementation contains a lot of boilerplate. However, it has one killer advantage: DDD assumes you have a team of dozens of people who barely understand each other's context, but individually must be able to assemble self-contained components. Swap "people" for "agents," and what do you get? Exactly: a multi-agent system for a large codebase!
\
Since I'm developing solo, my main bet was on aggressive automation and AI agents. For a long time, linters were intentionally ignored — the focus was on testing hypotheses and achieving a fast time-to-market.
Once, I started adding a fix for MCP clients so they could make tool calls asynchronously from the main agent loop. But something bad happened: I tasked the agent with implementing the feature, the feature was added, but the result? Well, the payback didn't take long.
The price for speed arrived right on schedule: Gemini simply couldn't add the changes properly without my help. One change broke the tests, another fixed those but broke others. The problem wasn't the model: I tried Claude Opus, and Codex also failed on the first try. It became clear: either I write documentation (which is essentially double the work), or I clean up the code so it becomes self-documenting.
It was time to pay down the technical debt, but first, I needed to understand its actual size. Let's take a look:
First, I added a very, very, VERY strict configuration for golangci-lint. 83 linters, configured to whip any code into perfect shape. Essentially, this configuration feels like the Rust compiler: one step out of line from perfect code, and the linter slaps your hands. (Shoutout to Ayaz Ayupov, who helped create this config for golangci; his work saved a lot of time avoiding building it from scratch).
Second, I decided to stash my current changes and see what the overall state of my project was. A basic baseline measurement (git checkout main && golangci-lint run ./...) highlighted the catastrophic scale of the accumulated tech debt: 2,611 issues. Qodo, the code review tool I use, kept warning me about this, but I ignored it. Now, ignoring it was no longer an option.
Let's calculate the ROI of manual labor. If we allocate a modest average of 3 minutes for thoughtfully reading, fixing, and verifying a single issue, a manual fix would take about 3 min * 2611 issues ≈ 130 hours of pure time. 130 hours! That's almost a month of full-time work spent exclusively on satisfying a static analyzer. And this is just my pet project! I don't want to be a hostage to my codebase; I want to build it! 🤯
So, I was faced with a challenge: bring the issue counter down to absolute zero (yes, I'm a maximalist) without freezing product development for a month. The obvious solution seemed to be delegating this routine to AI. But, as practice showed, the scale of the problem requires a systematic engineering approach, not just blindly throwing an agent at it.
Realizing the scale of the tragedy, I tackled the problem in the most obvious and lazy way worthy of any programmer: I decided to unleash the AI on the entire project at once.
My main tool right now is Antigravity — a fork of VSCode with absolutely nothing special about it, except the models are "free" if you have a Google AI subscription. (Important note: after experimenting with Cursor, Claude Code, Gemini CLI, and VSCode Copilot, I realized there is NOTHING that significantly distinguishes these editors from one another. So when I say "Antigravity," I genuinely mean any editor with an agent plugin). In Antigravity (as everywhere else), the agent works in the classic ReAct (Reason + Act) loop: it runs a command in the terminal, reads the output, analyzes it, and goes to edit the code. So let's ask the model to fix all the errors:
\
use `golangci-lint run --fix`, target is zero issues
\ That plan was great, it was freaking ingenious if I understand it correctly. it was a swiss watch. So, wasting no time, I went to get some coffee, made myself a cup, grabbed some snacks, came back, and… Quota for the next 5 hours exhausted! Wait, what? How?
\
The bottleneck wasn't the model's intelligence, but the "pipe" the data was flying through.
\
The linter, finding 2,611 errors, spat out a giant wall of text into stdout. The agent, following its loop, dutifully read these thousands of lines of output after every iteration. The context window instantly bloated to incredible sizes. The most frustrating part: Antigravity (like other editors) has a failsafe — if the output is too long, it packs it into a file and gives the model a link to it. But in my case, it didn't trigger: The golangci output was just slightly under the threshold for this protection to activate, so the entire raw output flew straight into the agent's context.
The model started "choking" on garbage context, which led to the inevitable: first, I blew through my 5-hour token limits, and then, after trying to restart the process, I hit the hard weekly API quotas. Fortunately, weekly quotas only apply to Pro models, so without wasting a second, I started thinking: how do I structure this process?
The conclusion was clear: feeding the entire technical debt to the AI at once is physically impossible. You simply won't have enough quotas. To automate this process, the AI agent needs a strict diet — a data flow filtration system and smart task routing. I needed to start managing the context.
To stay within limits, keep my sanity, and, most importantly, force the agent to work with surgical precision, the entire process had to be radically rebuilt. If the AI chokes trying to swallow an elephant whole, we will feed it in strictly measured bite-sized pieces.
I arrived at a system that can be called Double Isolation:
Lord, thank you that I wasn't afraid of boilerplate and a bloated codebase! I agree that DDD is ultimately an enterprise architecture, and doing it by hand might drive you crazy. But! A strict architecture with a clear segregation into layers (Domain, Infrastructure, Usecases) allowed me to algorithmically narrow the context. So I officially admit: ==sometimes, by increasing the codebase, you can win through logical transparency.== This isn't advice for every project (building libraries, protocols, etc., with DDD is a bad idea, trust me), but if we are talking about End User Software — the code that is directly used, not imported — it is a more than sound decision. But only if you aren't afraid of boilerplate and understand how to work with it correctly, OR if you have a team of at least 7-12 people (and we of course include multi-agent systems in this list).
Instead of throwing the agent at the whole repository, I started restricting its scope to one specific package. The prompt transformed from a global "fix everything" to a local, crystal-clear task:
\
fix linters at `./internal/domain/accounts/usecases`
\
This INSTANTLY solved the quota problem. The linter output for a single package might contain 80-100 errors, but it's certainly not 2,600. The agent calmly reads stdout, understands the context of the specific use case, makes an isolated fix, and moves on. Quotas were still consumed very quickly, but at this stage, the main advantage appeared: you can process at least 2-3 packages per quota and still have time for code review (which, by the way, is critically important, but more on that later).
But spatial isolation wasn't enough. Very quickly, I noticed that 83 linters are not a monolithic mass. They require a fundamentally different level of cognitive load from both a human and an AI. It's funny that at one point Gemini literally wrote to me: "I fixed warnings actually, but next time please split the linters up."
I broke all errors down into three categories (Tiers) and configured a specific agent pipeline for each.
unused, thelper, govet, nolintlint. It's syntactic garbage. The agent doesn't need to think about architecture here at all. We run it almost "blindly" across the entire package. It methodically deletes unused variables and places helpers in tests. Reviewing such pull requests takes seconds—you skim the diff and hit Approve.err113, wrapcheck, ireturn). Here, the AI needs to somewhat understand the call context. For example, when the agent rewrites the old github.com/pkg/errors to the standard fmt.Errorf("%w", err), it must not break the wrapping logic. The model handles this perfectly 95% of the time, but during review, you actually have to engage your brain.gocognit, funlen, nestif, cyclop. When a function balloons to 100 lines with 5 levels of nesting, the linter sounds the alarm. And here, the AI starts to blatantly "drift" and hallucinate. To satisfy the linter and reduce nesting, the agent is capable of ruthlessly shredding beautiful business logic into a dozen meaningless micro-functions, or worse, breaking architectural patterns.The difficulty here lies in composing the configuration properly so you can easily invoke the linter with either Tier 1 or Tier 3. I decided to be clever and generated the config via cuelang. It's not the only right solution, but it was convenient for me to have a single dynamic config that I could change as needed. Plus, you can add DO NOT EDIT or something similar to the YAML output so the agent doesn't even try to mess with the configs. By the way—NEVER allow the model to change configs on its own! Otherwise, the model gets "lazy" and starts modifying the config to bypass errors instead of fixing the code.
The biggest risk when refactoring 2,600 warnings is losing control over the changes, breaking package interface contracts, and consequently breaking dependent packages. We definitely don't want that, so we need some protection. Naturally, we need to review. But how do you review fixes that address so many issues? It's essentially reviewing the entire project from scratch!
The first option is to use the AI diff provided by all VSCode forks; the chat shows everything the model edited. However, after a while, you'll notice (or already have, if you use an IDE heavily!) that working with these diffs is extremely inconvenient:
git diff uses an algorithm that doesn't see logical rearrangements; git works with byte sequences, and uses byte comparison algorithm, not business logic.These are very serious problems. So much so that undertaking such a massive refactoring is simply dangerous, yet we want sufficient agency and autonomy from the neural network. What to do?
I took a different route. Since the agent makes changes directly to the files anyway, I started automatically "approving" all its edits in the editor, completely ignoring the AI diff. That diff hinders more than it helps. Besides, there is a more reliable, convenient, and popular solution: the classic git diff. In VSCode, it is incredibly convenient to stage changes not by whole files, but granularly, by individual hunks, clicking the "+" next to the required block. This allowed me to carefully separate the wheat from the chaff.
This is where the Triage matrix (mentioned earlier) came in handy. The process was divided into stages:
lll, varnamelen, wsl. Their fixes are predictable, don't break the structure, the order of code doesn't change, and the edits are very easy to review and stage with a standard git diff.gocognit, cyclop, funlen. Trying to reduce cognitive complexity, the agent regularly shuffled the order of declarations and radically changed function architecture. In a standard git diff, this turned into a bloody mess of empty and added lines. To understand the essence of the edits, I found two incredibly cool review tools: difftastic and SemanticDiffs. Their trump card is comparing files not as a set of bytes, but at the Abstract Syntax Tree (AST) level, showing logical differences rather than textual permutations.The work went DRAMATICALLY faster. While I previously passed the time between quotas by doing long, thoughtful reviews, after SemanticDiffs I could just skim to ensure the code looked good, check test coverage, and calmly approve the changes.
However, after some time, I noticed a new problem: the model started thinking, "If this is an internal package, then I can probably change this method entirely, and just fix it in the other importing packages!" But once the fix began, it completely forgot to search for ALL usages of a given function. For example: when I was refactoring the LLM client adapter, the model found a Stream() method that was poorly documented. Its logic: "Aha, well, since it's streaming, it's probably in the Kafka adapter. No? Well then I have nothing to change!"
While this example shows how crucial naming is (especially when AI writes code), it is very telling: the neural network didn't realize it was streaming a response from VertexAI, and that it was streaming text, not messages. But for some reason, it stubbornly refused to use grep across the entire project.
Teaching the model how to work with grep is inefficient: the command's output can be too large for the task's context, and such a search isn't really the right tool for checking compatibility anyway. So, I decided to use golang.org/x/exp/cmd/apidiff to verify that compatibility wasn't broken. This tool, although built by Golang contributors, has some usability quirks—the most serious being that it doesn't know how to work with git natively. But that is outweighed by the fact that it does its job absolutely perfectly. I started piecing it into a pipeline workflow:
# 1. Checkout a stable commit and export an API snapshot
go install golang.org/x/exp/cmd/apidiff@latest
git checkout <stable_commit>
apidiff -w ./path/to/pkg/api.diff ./path/to/pkg
# 2. Stash current changes (the agent's work), return to the branch, and pop changes
git stash
git checkout -
git stash pop
# 3. Compare the package's current API with the saved snapshot
apidiff ./path/to/pkg/api.diff ./path/to/pkg
Incompatible changes:
- NewToolID: changed from func(AccountID, github.com/google/uubid.UUID, ...ToolIDOption) (ToolID, error) to func(AccountID, github.com/google/uuid.UUID) (ToolID, error)
- ToolIDOption: removed
Don't be intimidated by the number of commands: you only need to go through the packages and do this once before starting the changes! Once you have the diff for the API, you can freely reuse it to verify changes while the neural network executes tasks.
In my case, I was slightly lenient with strict API adherence: since most changes occurred in internal/, 100% API preservation wasn't always required. But in public modules, there are situations where a linter (for example, unparam or the requirement to have context.Context as the first argument) is physically impossible to satisfy without breaking backward compatibility. In this case, the only thing you can do is instruct the model to use //nolint, but only with a detailed description and a strict review showing that it is a fundamental issue.
When facing Tier 3 structural linters, the AI always looks for the cheapest way to bypass the rule. And this search often leads to architectural destruction, premature optimization, or other problems that de facto reduce the model's autonomy.
Here's an example: trying to reduce the cognitive complexity of constructors, the agent repeatedly destroyed the Functional Options pattern. Instead of an elegant composition of options (designed for easy code generation), it simply "flattened" the arguments into one giant sheet of parameters, or transformed private types into public ones, just to avoid having to use //nolint. For example:
type newParams struct {
clientName string
trace trace.TracerProvider
}
type NewOption interface{ applyNew(*newParams) }
func New(ports ports.ServerStorage, oauth ports.OAuthHandler, opts ...NewOption) *Service {
params := newParams{
// imagine there are 20 params
}
for _, opt := range opts {
opt.applyNew(¶ms)
}
return &Service{
// and 20 fields more
}
}
In this example, it seems like there is nothing to change, but the model thinks: "Okay, funlen is complaining about a huge New(). The options take up most of it… What if I just pass everything directly in newParams?"
type NewParams struct {
Ports ports.ServerStorage
Oauth ports.OAuthHandler
ClientName string
Trace trace.TracerProvider
}
func New(params *NewParams) *Service {
return &Service{
// wow! so "easy" to read! (but model doesn't know WHY we used options before)
}
}
To the model, this is a genuinely cool improvement: less code in the constructor, less complex logic, all parameters are visible, everything is awesome! However, the options pattern exists for a reason: at the cost of considerable boilerplate, options protect object invariants, implement validation, and frankly look much cleaner than {a; thing; with; a; bunch; of; parameters} — assuming you aren't looking directly at the implementation. But the model blatantly ignores this.
There is a perfect solution to this problem: write a custom linter that checks adherence to the options pattern. However, we are dealing with a completely different task right now; why get distracted? A custom linter will definitely help in the long run, but writing it now? No, let's concentrate on reducing tech debt, and leave the process of maintaining it for the end.
To prevent the agent from breaking the design, I started introducing architectural anchors. First, I began writing a "Technical Specification for Linter Development." However, it's not a full-blown tech spec, but rather a simpler version, focused on what I want to get and what I don't want. I wrote using this template:
# REQUIREMENTS FOR <your-issue> LINTER
## CONTEXT
**purpose, why it's important for your project**
## EXAMPLES
Good:
~~~go
func DoX() {
// your example. Make code example as short as it is even possible
}
~~~
Bad:
~~~go
func DontDoY() {
// your example. Keep these examples EXTREMELY BRIEF
}
~~~
# REFACTORING RECOMMENDATIONS
**This one is most crucial part: You don't have to write linter, if
you explain carefully HOW to refactor. Later it will help AI to
write a linter implementation for you.**
Examples:
- Issue: **Brief explanation of some case**
- Solution 1: **keep it short**
- Solution 2: **allow AI to choose solution**
And that's it! You can check my project to see how I implemented these rules. Just making a tech spec isn't enough; you need to add it to the Rules for your editor. Directories vary between editors, but the principle remains: for example, in Antigravity, this spec goes into .agents/rules/go-quality-your-case.md. Usually, the agent will figure out on its own that it needs to use this rule, but it's still better to be explicit: in AGENTS.md (or your rules file), specify something like this:
## ARCHITECTURAL CONSTRAINTS
While refactoring, editing, or designing implementation, you **MUST** read
these rules first, before doing any changes:
- `.agents/rules/go-quality-your-case.md` — extremely brief description
- `.agents/rules/go-quality-option-pattern.md` — usage and design of Golang
Functional Options pattern
- `.agents/rules/go-quality-error-handling.md` — error handling rules for Golang
Writing architectural rules in Markdown is an excellent way to quickly pay down tech debt (which is what allowed me to finish in 3.5 days of work instead of a month). But in the long run, any prompt remains merely a statisticalrecommendation. The model might ignore it as soon as the probabilistic weight of another solution tips the scales, or if the task's context becomes so bloated that there's simply nowhere to inject these rules.
The true value of this approach is revealed later. A text constraint from AGENTS.md is a ready-made tech spec for writing a custom linter. Once the mass refactoring is complete, you simply feed this exact same text to the agent with a new, highly specific task: "Write a custom static analyzer based on go/analysis that checks for adherence to the Functional Options pattern." It will likely take a couple of days of work, even with AI. But in return, you get all the benefits at once:
Thus, a temporary crutch for the AI elegantly converts into an ironclad algorithmic defense for your CI/CD.
We are used to evaluating automation by the time saved. And by this metric, the AI performed brilliantly: 2,611 errors were reduced to zero in 3.5 days. For a human, doing this volume of thoughtful review would take about 130 hours of pure time. That's almost a whole working month that could be spent on product development, or simply spending time with family—we are all human, after all. But the most important thing happened after I finally brought the error count to zero: when I asked the agent to add asynchronous tool calls post-refactoring, the model successfully wrote the feature implementation on the first try! My weekend wasn't wasted; I got not only clean, readable code, but also made it vastly easier for the models to generate new features.
==But the main takeaway from this experiment is qualitative, not quantitative.==
The role of a Senior Engineer when working with agents is transforming: we are shifting from writing code to building system constraints. Throw an AI into a project without control, and it will rewrite everything so badly it breaks half your services. Wrap its work in a Triage matrix, protect the ports via apidiff, and anchor the architecture—and you get the perfect executor, capable of digesting any volume of routine.
Models are ready for massive refactoring of complex Enterprise projects, and you don't even need a team for it! But only as long as you algorithmically manage the context they consume.
Cleaning up tech debt with AI agents is already a viable working tool for maintaining bloated codebases. How do you integrate neural networks into your processes? Have you encountered situations where the AI broke contracts trying to "improve" the architecture?
I'd be glad to discuss your experiences and approaches. You can reach out to me on LinkedIn or check out my current work on the Cynosure project on GitHub.
2026-04-07 14:11:16
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2026-04-07 11:34:14
The word “decentralization” evokes free, distributed, open systems available for everyone and not controlled by a single party. However, that doesn’t mean nobody is in charge of anything. Beyond the curtains, someone writes and updates the code, manages the treasury, handles the “official” channels. Every active crypto network has a team behind it, and its organization sometimes has a huge impact on how decentralized (or not) that network is. They’re usually led by either companies or foundations.
A company is a legal business created to make money for its owners or shareholders. It has managers, employees, and clear leadership, and it must follow the commercial laws of the country in which it’s registered —or else. A foundation is often a nonprofit organization set up to support a mission, not to distribute profits, and it operates under its own governing rules and local regulations.
Each entity has its own advantages and tradeoffs for different people. However, in crypto, we’re especially interested in the decentralized factor. The structure behind the scenes influences who holds power, who funds development, and how upgrades move from idea to reality. Let’s see more of this.
In this kind of structure, a registered company (somewhere) builds and steers the entire ecosystem. They have an investment to recover, they have budgets and employees. They need to comply with local laws, not only in the place where they are registered, but also in the countries where their service or platform reaches. So, for instance, it doesn’t matter if Binance is registered in the Cayman Islands; they need to comply with European laws if they want to keep European clients.
This can bring order. When a bug appears, a coordinated (and paid) team can respond quickly. When a major upgrade is proposed, leadership can align resources and move forward. There’s also a clear legal entity, which regulators and partners can point to. That clarity can help the ecosystem expand into the traditional financial world.
Now, that may also be a huge problem. Cryptocurrencies were created to avoid this exact situation, indeed. If a government or hostile central party can blame a single entity (a company) for anything they don’t like, that entity could be easily shut down. Besides, if not every single time, in many of those cases, their products can go down with the brand. It’s happened before, several times. Decentralization isn’t only about how many computers run the software, but about who controls that software.
To be fair, most companies behind crypto networks ensure they don't control the software and the network by creating platforms with various decentralized mechanisms, ultimately in the hands of the community. However, this doesn't mean they don't still wield significant influence in these ecosystems. If they control a large portion of the tokens, their voice can outweigh the broader community. The network may be distributed in code, yet guided by a small circle of decision makers.
As we’ve mentioned above, foundations are mission-driven, not profit-driven. However, it’s usual that they hold part of the project’s tokens in a treasury. They use those funds to give grants to developers, researchers, and community projects. A board or council oversees decisions, following local nonprofit laws. It doesn't have shareholders, but it can still influence direction through funding and coordination.
In these systems, proposals are often published openly. Developers debate changes in public forums and token holders may vote, depending on the design. The foundation may not be employing every core contributor, which can spread development across multiple groups. And this is important: if the foundation disappears, the software remains. Other teams and the community could take the reins. That’s not always the case with companies.
On the other hand, foundations can still hold significant token reserves, and this can give them more influence over the ecosystem. The difference with companies is that, in many jurisdictions, foundations are mission-driven by law: they can only use these funds to benefit their own mission (their own ecosystem).
It’s inevitable: behind every crypto network, there are people making decisions. What matters is how the system was designed from the start. If governance tools are built into the protocol, such as on-chain voting or transparent proposal systems, the community can shape upgrades directly. A fair consensus mechanism also plays a role, since it decides who accepts/rejects transactions and how power spreads across participants. When these features are strong, no single company or foundation can quietly take over, because rules are enforced by the network itself.
Obyte has its own foundation, aimed solely at supporting this ecosystem. However, its network was built as autonomous. The Directed Acyclic Graph (DAG) design removes miners and "validators "altogether, so there are no middlemen competing to approve anything. Users independently add their own transactions, which reduces reliance on a specialized group. On top of that, Obyte includes on-chain governance, allowing token holders to participate in key decisions.
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In the end, a company can bring strong coordination, while a foundation can encourage broader participation. Neither label guarantees decentralization: it's important for the community to check who controls what, and participate by themselves.
A label alone won't tell how decentralized a network is. So, it helps to look under the hood: start by checking who controls key pieces of the ecosystem. For example, who holds a large share of the tokens? Who funds core development? Who proposes and approves upgrades? Are treasury transactions public on chain?
Most of this can be verified through explorers, governance forums, GitHub repositories, and official documentation. Some media may talk about it, too. Token distribution dashboards and transparency reports can also reveal whether power is concentrated or spread out.
Participation matters just as much as observation. Creating an account in governance forums (or social channels), checking the voting platforms, reading proposals before voting, and supporting independent developers are practical first steps. Decentralization isn't a passive feature. It grows when community members show up, ask questions, and use the governance tools available to them.
Featured Vector Image by vectorpouch / Freepik
