2026-01-26 00:31:28
Trust. Governance. What was the third one… Legitimacy. It’s not a prerequisite for trust or governance, but rather the condition that decides their limits.
Digital trust is often discussed as if it were a moral achievement. If users stay, if adoption grows, if systems function at scale, then trust must exist - and ==if trust exists, legitimacy is assumed to follow==. This assumption is very comfortable. It allows platforms, institutions, and infrastructures to treat trust as an output of performance rather than as a consequence of authority that has been earned.
But comfort is not accuracy.
==Trust and legitimacy are not the same thing==. They do not arise from the same conditions, they do not perform the same function, and - crucially - they do not fail in the same way. Digital systems have become remarkably good at generating trust without legitimacy. They have become far less capable of surviving what follows.
This distinction matters now because digital platforms no longer merely mediate interaction. They allocate visibility, enforce rules, resolve disputes, exclude participants, and reshape incentives. They govern. And governance without legitimacy is not neutral - it is fragile.
The argument itself tends to ==divide people who build systems from those who live under them==.
Trust, in its most basic sense, is instrumental. It is a mechanism humans use to reduce complexity in situations ==where full knowledge is impossible==. According to Niklas Luhmann, trust allows action in the absence of certainty. It does not require moral approval. It does not require fairness. It requires only a sufficiently stable expectation that the system will behave as anticipated. But… will it behave?
This is why trust can exist in deeply asymmetric, unequal relationships. ==Users trust platforms they do not understand==. Citizens trust institutions they do not control. Workers trust systems that can penalize them, in the end. Examples would trigger endless disputes, so - at least for now - let’s stop here. The bottom line is that trust arises not because power is justified, but because outcomes are predictable enough to navigate through conveniently. And feel somehow secure in that sailing (we’ll touch on security versus convenience a bit more in the future).
Digital systems excel at this. Interfaces are consistent. Processes are automated. Friction is minimized. Over time, ==habit replaces evaluation==. Trust becomes procedural - not reflective, not consensual, but functional. This is where we should pay some attention.
There is nothing inherently wrong with this. Trust has always operated this way. What is often misunderstood is what trust does not provide, mainly trust does not grant authority. It does not confer moral standing, also. And it does not imply consent. Remember all those clickwrap agreements you submit everyday without understanding actually what you’re agreeing to?
Legitimacy, by contrast, is normative. It concerns whether power ought to be exercised, not whether it can be navigated. It asks whether rules are justified, ==whether decision-makers are accountable==, and whether those subject to authority have reason to recognise it as rightful.
A system can be trusted and illegitimate at the same time. History offers countless examples, mainly in politics, but not limited to. Digital platforms are simply the most recent and the most efficient these days.
One of the most persistent errors in digital governance is the belief that continued participation implies agreement. If users stay, the logic goes, they must accept the rules. If they accept the rules, the system is legitimate. Ditto.
This reasoning collapses the moment power asymmetry is taken seriously.
Social contract theory has long distinguished between compliance and consent. Hobbes understood obedience as a condition of order, not legitimacy. A look back reveals that Locke insisted that authority remains conditional - tolerable only so long as it serves those governed. Rousseau went further, arguing that ==legitimacy requires participation in the formation of the rules themselves==.
Digital platforms quietly bypass these distinctions - who reads philosophers these days? Participation is measured, not deliberated. Consent is embedded in terms of service. Exit is theoretically available but practically constrained by lock-in, network effects, professional dependency, or social cost. The last one is bigger ==the longer the service is used==. What seems to be Kensington High Street at first sight may quickly become Kensington Avenue.
The result is a form of coerced continuity: users remain not because they agree, but because alternatives are absent, impractical, or invisible. Trust persists because daily interaction demands it. Legitimacy remains unexamined because questioning it offers no clear remedy.
This is why metrics of engagement, retention, or satisfaction cannot substitute for legitimacy. They measure adaptation to power, not acceptance of it. They reveal how well users cope with governance, not whether governance is justified.
Mistaking usage for consent is not a neutral analytical error. ==It allows systems to expand authority without ever confronting its moral basis==.
Digital systems often compensate for legitimacy deficits through performance. As long as platforms are fast, convenient, and effective, trust fills the gap. Users tolerate opaque rules because outcomes remain favourable. Institutions avoid justification because efficiency silences dissent.
This strategy works. Until it doesn’t.
Trust can mask illegitimacy, but it cannot erase it. Instead, it accumulates what might be called legitimacy debt. Each unilateral rule change, unexplained decision, or unchallengeable enforcement action draws against a reserve that trust temporarily supplies.
The problem is not gradual erosion. ==Trust rarely collapses slowly==. It breaks when expectations collide with power. Moments of crisis - moderation disputes, data misuse, sudden policy shifts, unexplained exclusions - expose the underlying structure. At that point, trust no longer reduces complexity. It amplifies betrayal.
Legitimacy fails differently. It does not depend on flawless outcomes. It depends on the justification. Systems with legitimacy can survive error because they can explain themselves, correct themselves, and be challenged without unraveling.
Systems without legitimacy have only performance to defend them; or convenience offered to its users (quite often misused, isn’t?). When performance or convenience falter, there is nothing beneath it. This also why many platform are paranoid about its performance and convenience (often called excellent user experience).
This is why digital trust crises often appear sudden and disproportionate. But, they are not sudden at all, they are deferred.
At this point, it is tempting to retreat into familiar solutions: transparency dashboards, better UX, clearer communication, smarter systems. These are certainly useful, very useful and needed, but none of them address legitimacy.
The problem is not that we lack trust, as Onora O’Neill has argued, but that ==we conflate trustworthiness with reliability==. Reliable systems can still be unaccountable. Transparent processes can still be unjustified same way as automation can obscure responsibility rather than clarify it.
Legitimacy requires governance structures that constrain power, not merely optimise it. It requires identifiable authority, predictable rules, and mechanisms for contestation. It requires the possibility of saying “no” - and being heard. And address that “no” when it’s being heard.
This is highly uncomfortable in digital contexts because it challenges the fiction of neutrality. Platforms often present themselves as technical systems rather than governing institutions (they only allow, they only enable, they only offer the choice or possibility). But governance does not disappear when it is denied. ==It becomes unaccountable==.
The critical point is this: trust can motivate cooperation, but only legitimacy can justify obligation. When interests align, trust is enough. When they diverge, only legitimacy holds.
Digital systems do not face a crisis of trust. They face a ==crisis of legitimacy== that trust has temporarily postponed.
The real question is not whether users trust platforms today. It is whether platforms are prepared to justify their authority tomorrow - when trust is no longer sufficient, when conflict emerges, when performance or convenience no longer compensates for power.
Trust buys time. Legitimacy buys durability.
As digital infrastructures continue to govern more aspects of economic, social, and civic life, the distinction will become harder to ignore. Systems that confuse trust for consent will discover that cooperation can vanish overnight. Systems that ground authority in legitimacy will survive disagreement, error, and change.
Digital trust does not fail because people stop believing. ==It fails when systems refuse to justify why they should be believed in at all==.
\ \
2026-01-26 00:03:17
How are you, hacker?
🪐 What’s happening in tech today, January 25, 2026?
The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, The World Health Organization's Executive Board was founded in 1949, Soviet Russia conducted the first succesful test of an Intercontinental Ballistic Missile in 1959, Nasa's Opportunity Rover landed on Mars in 2004, and we present you with these top quality stories. From HARmageddon is cancelled: how we taught Playwright to replay HAR with dynamic parameters to Patterns That Work and Pitfalls to Avoid in AI Agent Deployment, let’s dive right in.
By @zbruceli [ 18 Min read ] A deep dive into the Internet Archives custom tech stack. Read More.
By @bernard [ 4 Min read ] I believe that AI’s impact and future pathways are overstated because human nature is ignored in such statements. Read More.
By @denisp [ 23 Min read ] Success isnt building the agent; its managing it. From AgentOps to ROI dashboards, here is the operational playbook for scaling Enterprise AI. Read More.
By @denisp [ 17 Min read ] Avoid the AI Slop trap. From runaway costs to memory poisoning, here are the 7 most common failure modes of Agentic AI (and how to fix them). Read More.
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By @dataops [ 4 Min read ] DataOps provides the blueprint, but automation makes it scalable. Learn how enforced CI/CD, observability, and governance turn theory into reality. Read More.
By @socialdiscoverygroup [ 19 Min read ] We taught Playwright to find the correct HAR entry even when query/body values change and prevented reusing entities with dynamic identifiers. Read More.
🧑💻 What happened in your world this week?
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2026-01-25 15:11:06
How are you, hacker?
🪐Want to know what's trending right now?:
The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here.
## AI Doesn’t Mean the End of Work for Us
By @bernard [ 4 Min read ]
I believe that AI’s impact and future pathways are overstated because human nature is ignored in such statements. Read More.
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🧑💻 What happened in your world this week? It's been said that writing can help consolidate technical knowledge, establish credibility, and contribute to emerging community standards. Feeling stuck? We got you covered ⬇️⬇️⬇️
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.gif)
2026-01-25 09:59:59
Why wider neural networks usually break—and how a simple mathematical constraint lets deep models grow smarter without collapsing.
2026-01-25 03:00:19
Some recent trends in theoretical ML
2.1 Deep Learning via continuous-time controlled dynamical system
2.2 Probabilistic modeling and inference in DL
3.1 Kuramoto models from the geometric point of view
3.2 Hyperbolic geometry of Kuramoto ensembles
3.3 Kuramoto models with several globally coupled sub-ensembles
Kuramoto models on higher-dimensional manifolds
4.1 Non-Abelian Kuramoto models on Lie groups
4.2 Kuramoto models on spheres
4.3 Kuramoto models on spheres with several globally coupled sub-ensembles
5.1 Statistical models over circles and tori
5.2 Statistical models over spheres
5.3 Statistical models over hyperbolic spaces
5.4 Statistical models over orthogonal groups, Grassmannians, homogeneous spaces
6.1 Training swarms on manifolds for supervised ML
6.2 Swarms on manifolds and directional statistics in RL
6.3 Swarms on manifolds and directional statistics for unsupervised ML
6.4 Statistical models for the latent space
6.5 Kuramoto models for learning (coupled) actions of Lie groups
6.6 Grassmannian shallow and deep learning
6.7 Ensembles of coupled oscillators in ML: Beyond Kuramoto models
Examples
7.2 Linked robot’s arm (planar rotations)
7.3 Linked robot’s arm (spatial rotations)
7.4 Embedding multilayer complex networks (Learning coupled actions of Lorentz groups)
In 1994. Watanabe and Strogatz [69] demonstrated that the simple Kuramoto ensemble with globally coupled identical oscillators exhibits 3-dimensional dynamics. They have shown that dynamics of a large ensemble can be reduced to the system of ODE’s for three global variables. This implies that an ensemble consisting of N oscillators admits N − 3 constants of motion. This result initiated the new research direction which deals with symmetries and invariant submanifolds in simple Kuramoto networks.
\ The underlying symmetries have been exposed in 2009. by Marvel, Mirrolo and Strogatz [70].
\
\ Proposition 1. [70]
\
\ Further insights into the relation between hyperbolic geometry and Kuramoto models have been reported in [71]. It has been demonstrated that (under certain conditions on the coupling function f) Kuramoto dynamics of the form (6) are gradient flows in the unit disc with respect to hyperbolic metric. Potential function for dynamics (7) has particularly transparent geometric interpretation. It turns out that dynamics of Kuramoto ensembles with repulsive interactions uncover a point inside the unit disc that has the minimal sum of hyperbolic distances to the initial points on S1. In complex analysis this point is conformal barycenter [72] of the initial configuration.
\
:::info Author:
(1) Vladimir Jacimovic, Faculty of Natural Sciences and Mathematics, University of Montenegro Cetinjski put bb., 81000 Podgorica Montenegro ([email protected]).
:::
:::info This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.
:::
\
2026-01-25 03:00:12
Today the Go team is pleased to release Go 1.25. You can find its binary archives and installers on the download page.
\
Go 1.25 comes with improvements over Go 1.24 across its tools, the runtime, compiler, linker, and the standard library, including the addition of one new package. There are port-specific changes and GODEBUG settings updates.
\
Some of the additions in Go 1.25 are in an experimental stage and become exposed only when you explicitly opt in. Notably, a new experimental garbage collector, and a new experimental encoding/json/v2 package are available for you to try ahead of time and provide your feedback. It really helps if you’re able to do that!
\ Please refer to the Go 1.25 Release Notes for the complete list of additions, changes and improvements in Go 1.25.
\ Over the next few weeks, follow-up blog posts will cover some of the topics relevant to Go 1.25 in more detail. Check back in later to read those posts.
\ Thanks to everyone who contributed to this release by writing code, filing bugs, trying out experimental additions, sharing feedback, and testing the release candidates. Your efforts helped make Go 1.25 as stable as possible. As always, if you notice any problems, please file an issue.
\ We hope you enjoy using the new release!
Dmitri Shuralyov, on behalf of the Go team
\ This article is available on The Go Blog under a CC BY 4.0 DEED license.
\ Photo by Francesco Gallarotti on Unsplash
