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Media analysis has always been data-rich and insight-poor. PR teams sit on top of dozens of signals—traffic dashboards, SEO tools, internal notes, past campaign results. Yet when it comes to choosing where to publish, decisions often feel uncertain. The data is there, but it does not connect.

Outset Media Index (OMI) approaches this problem at its root. It does not add another metric. It reorganizes how media outlets are evaluated.

The Bottleneck in Media Planning

Most PR workflows look structured on the surface. There are tools for outreach, monitoring, and reporting. Campaigns move, coverage is tracked, dashboards update in real time.

But the critical decision happens earlier. Before a single pitch is sent, teams need to decide which outlets are worth targeting and which placements align with campaign goals. This is where the process slows down.

A typical evaluation involves multiple tabs:

traffic data from one platform

SEO indicators from another

editorial checks done manually

internal assumptions layered on top

None of these signals are wrong. They are simply disconnected. The result is familiar: time-consuming research and decisions that rely as much on experience as on data.

What Outset Media Index (OMI) Does Differently

Outset Media Index starts from a simple premise: media analysis should function as a system, not a collection of signals.

It consolidates fragmented data into a unified analytical framework and analyses outlets using more than 37 metrics.

These metrics are not treated equally or in isolation. They are structured to reflect how media actually performs across multiple dimensions:

reach and audience quality

engagement and interaction

SEO and LLM visibility

editorial flexibility

syndication and influence

Instead of asking “Which outlet has more traffic?”, OMI reframes the question:

“What role does this outlet play in the information ecosystem?”

That shift changes the outcome.

From Scattered Inputs to a Coherent View

The most immediate difference is clarity. Where traditional workflows require assembling insights manually, OMI presents a consolidated view. Metrics are normalized, meaning they can be compared directly without additional interpretation.

Rather than reconciling numbers across tools, teams can evaluate outlets side by side within a single framework. Rankings, scores, and profiles are built on consistent logic. As a result, analysis becomes faster, but more importantly, it becomes repeatable.

Why Traffic Alone No Longer Works

For years, traffic has been the default proxy for media value. It is easy to understand and easy to measure.

It is also incomplete.

Two outlets with similar traffic can perform very differently:

one may drive engagement, the other passive views

one may be widely cited, the other rarely referenced

one may trigger syndication, the other remain isolated

OMI captures these differences explicitly.

It introduces metrics that reflect how visibility actually spreads:

Syndication depth — how content is republished across networks

Share of LLM citations — how content surfaces in AI-generated answers

These factors define modern media impact. OMI integrates them into a single model, reducing the need to infer influence indirectly.

Built for Decisions, Not Exploration

Many analytics platforms are excellent at exploration. They allow users to dig into data, build custom views, and extract insights.

That flexibility comes at a cost: complexity.

OMI is designed differently. Its outputs are structured for decisions.

Instead of raw datasets, users work with:

ranked outlet lists

comparative scoring systems

detailed media profiles

customizable shortlists

The goal is not to analyze indefinitely. It is to move from evaluation to action with minimal friction.

In practice, this reduces:

time spent on research

reliance on subjective judgment

inconsistency across campaigns

Adding Context With Outset Data Pulse

Numbers rarely speak for themselves. Trends need interpretation.

OMI is supported by Outset Data Pulse, a reporting layer that connects metrics into a broader narrative. It tracks how media signals evolve and explains shifts in:

audience behavior

distribution patterns

editorial strategies

This adds a layer of context that most tools lack. Teams can see not only how outlets perform, but how that performance changes over time—and why it matters.

Final Thought

Outset Media Index sets a new standard by addressing a gap that has been largely overlooked.

The PR industry has optimized how campaigns are executed and measured. It has not standardized how media decisions are made.

By turning fragmented data into a coherent system, OMI makes those decisions more transparent, more consistent, and more defensible.

That is where its value lies—not in adding more data, but in making the existing data usable.

As of mid-April 2026, the "Machine Economy" is no longer a theoretical concept—it is a measurable market force. With AI agents now executing millions of autonomous transactions via the x402 protocol and decentralized GPU demand reaching all-time highs, the "Web3 Backend" sector is facing a critical technical crossroads. The Graph (GRT) and Akash Network (AKT) have emerged as the primary infrastructure proxies for this rotation: one serving as the "Google of Blockchains" for AI agents, and the other as the "Open-Source AWS" for their compute needs.

The Graph (GRT): Early Recovery, Still Heavy Overhead

 

Source: tradingview 

The Graph is currently undergoing its most significant evolution since inception: the rollout of the Horizon Protocol. Following the December 2025 transition to a modular architecture, GRT has moved beyond simple subgraphs. The Q1 2026 launch of the x402-compliant Subgraph gateway has officially enabled AI agents to autonomously query and pay for data in real-time, effectively turning GRT into the primary data layer for the "Agentic Web."

Technically, GRT is in an "early basing" phase. At $0.025, it has successfully reclaimed its 7-day ($0.0243) and 30-day ($0.0246) moving averages, but it remains heavily suppressed by its 200-day average ($0.0426). With a 99% drawdown from its peak, the token is fighting through massive overhead supply from multi-year bagholders.

GRT Price Scenarios:

Base Case: Sideways oscillation in a -20% to +30% band (approx. $0.020–$0.033). AI/data headlines are providing a floor, but the market is waiting for evidence of sustained query fee burns from AI agents before a full re-rating.

Bullish Path: A "Web3 Backend" rotation targeting $0.035–$0.045 (+40% to +80%). This would require a clean break above the 200-day SMA, likely triggered by a surge in Amp (The Graph's new enterprise SQL database) adoption by institutional fintechs.

Bearish Path: A rotation fade toward $0.016–$0.019 (-25% to -35%). If capital flows back into high-performance L1s, GRT's weak long-term trend makes it vulnerable to one more "flush" before a final bottom.

Akash Network (AKT): Firmer Trend, Testing The 200-Day SMA

Source: tradingview 

Akash Network is currently reaping the rewards of the Burn-Mint Equilibrium (BME) activation on March 22, 2026. This economic milestone, powered by Pyth oracles, has finally stabilized compute pricing in USD terms, making it viable for enterprise-grade AI pre-training. Furthermore, Akash's support for NVIDIA Blackwell (B200/B300) GPUs has allowed decentralized providers to capture high-scale workloads that were previously exclusive to hyperscalers like AWS.

Structurally, AKT is much stronger than GRT. At $0.51, it is actively testing its 200-day SMA ($0.509) from below. While the MACD histogram (-0.0022) suggests a minor momentum cool-off after its 11% weekly pump, the price is holding firm, indicating that a breakout attempt is in the works.

AKT Price Scenarios:

Base Case: Constructive range-play between $0.41 and $0.66 (-20% to +30%). Dips toward the $0.45 (7-day SMA) are likely to be bought by those betting on the upcoming Confidential Computing (AEP-65) rollout.

Bullish Path: An AI compute leg targeting $0.68–$0.82 (+35% to +60%). A clean break and hold above the 200-day MA would signal a total trend reversal, potentially driven by the announcement of the Shared Security partner (Cosmos vs. Solana) for the Akash L1.

Bearish Path: A rejection at the 200-day line leading to a slide toward $0.33–$0.38 (-25% to -35%). This is the risk if GPU utilization rates drop or if hardware supply chains for Blackwell chips favor centralized clouds in the short term.

Conclusion

The "Web3 Backend" trade is currently a tale of two different technical stages. Akash (AKT) is the clear leader, showing a visible recovery and testing its long-term trendline on the back of real-world compute demand. The Graph (GRT) is the lagging, "deep value" play that offers higher optionality if the AI agent query narrative gains mass adoption.

If AI infrastructure capital continues to expand through Q2 2026, AKT is the more likely candidate to lead the next leg higher, while GRT remains a high-beta catch-up play. If the narrative stalls, both are likely to stay in wide ranges, with AKT retreating to its averages and GRT drifting back into its search for a permanent floor.

 

Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.

As we move through mid-April 2026, the high-performance Layer-1 (L1) sector is witnessing a sharp divergence in narrative and technical strength. NEAR Protocol has successfully pivoted from a "Solana alternative" to the primary orchestration layer for the Agentic Web, while Sonic (the successor to Fantom) is attempting to prove its "vertically integrated" ecosystem model. With both chains rolling out fresh DeFi incentives and hitting new TVL milestones, the market is deciding if this is the start of a sustained bull leg or merely a liquidity-driven spike.

NEAR: The AI-Native L1 Sentiment Leader

 

Source: tradingview 

NEAR Protocol is currently the darling of the "User-Owned AI" narrative. Since the Halving Upgrade in late 2025 slashed annual inflation to 2.5%, the network’s economics have tightened significantly. Sentiment is further bolstered by the Grayscale Spot NEAR ETF filing earlier this year and the launch of Near.com, a consumer-facing super-app for AI-driven cross-chain swaps. Technically, NEAR is trading above its 7-day ($1.38) and 30-day ($1.29) averages, signaling an emerging uptrend within its broader cycle.

NEAR Price Scenarios:

Base Case: Sideways to bullish oscillation within a -15% to +35% band (approx. $1.18–$1.88). As long as the Intents fee buyback mechanism (burning 100% of cross-chain fees) keeps demand steady, the $1.20 support should hold.

Bullish Path: A high-performance L1 leg targeting $2.10–$2.45 (+50% to +75%). This would require a sustained daily close above the 200-day SMA ($1.70), likely triggered by a surge in "Agentic Web" transaction volume.

Bearish Path: A "pop and drop" fade toward $0.95–$1.10 (-25% to -35%). If the AI narrative loses steam or ETF progress stalls, NEAR risks mean-reverting back to its late-2025 lows.

Sonic (S): Vertically Integrated, Speculative Base

Source: tradingview 

Sonic (S) has moved past its "Fantom migration" phase and is now branding itself as the highest-throughput EVM chain (100k+ TPS). The recent launch of the US Sonic Dollar (USSD)—a stablecoin backed 1:1 by BlackRock-managed T-bills—has provided a massive boost to on-chain liquidity. Furthermore, Binance's recent staking of 76M S tokens has added a layer of institutional credibility. Despite this, the token remains in a "tentative basing" phase, trading far below its long-term trend.

Sonic (S) Price Scenarios:

Base Case: Volatile range-play between $0.033 and $0.062 (-25% to +40%). Sonic’s thin liquidity makes it prone to sharp percentage pops on news, followed by equally swift retracements.

Bullish Path: A speculative re-rating toward $0.07–$0.085 (+60% to +90%). This targets the 200-day SMA ($0.095) and would be driven by the public release of the Spawn AI smart contract generator.

Bearish Path: A failed breakout leading to a retest of $0.025–$0.030 (-30% to -45%). If the "vertically integrated" model fails to generate significant daily fees, the "dead chain" narrative may resurface.

Conclusion

NEAR Protocol currently offers the more structurally sound setup, with its AI-native fundamentals and Grayscale-backed narrative supporting a credible new uptrend. Sonic is the high-beta alternative—capable of massive percentage gains from its low base but carrying significantly higher volatility. If the market continues to favor "Fast L1 + DeFi TVL" themes through Q2 2026, NEAR is your likely leader, while Sonic remains a high-reward satellite play for those betting on the "vertically integrated" niche.

 

 

Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.

A mainnet launch proves the technology works. It is the strongest credibility signal a blockchain project can produce. Most projects announce mainnet with a blog post and a tweet, then move on. 

Most projects spend their mainnet launch focused on the technical side and treat mainnet launch PR as an afterthought. The ones that get it right run three phases: build the story before launch, coordinate the announcement, and keep the narrative alive after the network goes live.

Why Mainnet Is the Most Underused PR Moment in Crypto

A mainnet launch stands apart from other crypto milestones because it is a pure product story. It does not involve token speculation, financial returns, or fundraising claims. This makes it editorially attractive to journalists who avoid covering token promotions.

Tier-1 outlets prefer product stories over token announcements. A mainnet announcement PR gives them something they can cover without regulatory risk. 

The story also translates beyond crypto. Mainstream tech publications cover significant infrastructure launches in a way they rarely cover token events.

Mainnet creates a verifiable proof point. Anyone can check a blockchain explorer and confirm the network is live. This on-chain verifiability gives the story a factual anchor that press releases lack. 

Outset PR's approach to shaping stories that win crypto journalists applies directly here: journalists respond to stories with factual anchors and clear significance, not promotional framing.

Phase 1: Pre-Launch Teaser Cadence (Four to Two Weeks Before)

The goal is to build anticipation without announcing the launch date prematurely.

Four weeks out: Place a technical deep-dive that explains what the network does and why it matters. Target developer-focused outlets (The Block, Blockworks) and crypto-native publications (CoinDesk, Decrypt). Focus on the architecture, not the token.

Three weeks out: Publish founder commentary on the broader trend the mainnet addresses: scalability, interoperability, privacy, or RWA infrastructure. This positions the project within a narrative journalists already follow.

Two weeks out: Release testnet results, audit completions, or performance benchmarks as standalone news items.

Do not announce the exact launch date until the embargo phase. Do not lead with the token. Crypto product launch PR works because it is a product story. Adding a token promotion dilutes the editorial appeal.

Outset PR's Press Office model fits this phase because it generates a steady cadence of founder commentary and expert quotes between milestones.

Phase 2: The Embargo and Coordinated Announcement (Launch Week)

Every action during launch week determines whether the mainnet generates compounding coverage or a single-day spike.

Send embargoed press kits to 5 to 8 selected journalists seven days before launch. Include a technical fact sheet (architecture, consensus, throughput, audit results), founder interview availability, visual assets (network diagrams, explorer screenshots), and a clear embargo lift time synced to the mainnet going live.

Coordinate the embargo lift so all coverage publishes within a two-hour window. When multiple outlets cover the same story simultaneously, it triggers aggregator pickup across CoinMarketCap, Google News, and Binance Square.

On launch day, community channels share earned coverage as it appears, not the press release itself. Monitor for factual errors and correct within the first hour.

Outset PR tracked how this coordinated density works through its ChangeNOW ecosystem campaign: 600+ articles and 100+ expert quotes produced coverage density that aggregators and AI systems picked up as a coherent narrative.

Phase 3: Post-Launch Narrative Continuation (Two Weeks to Three Months After)

Most projects go silent after mainnet. The coverage stops, the narrative defaults to price charts, and the credibility window closes. The strongest blockchain launch communication strategy keeps the story alive across three stages.

Week 1 to 2: Place follow-up stories covering first-week metrics: transactions processed, wallets created, dApps deployed. These data points prove the network works under real conditions.

Week 3 to 4: Secure thought leadership placements where the founder analyses what the launch revealed about scaling challenges, cross-chain dynamics, or developer tooling gaps.

Months 2 to 3: Shift to ecosystem coverage. Every partnership, integration, and dApp deployment on the new mainnet is a standalone PR story that compounds search authority.

Five follow-up articles across CoinDesk, Decrypt, and The Block create a coverage cluster that AI systems interpret as sustained editorial interest. 

That cluster determines whether the project appears in AI-generated answers six months later. Outset PR's research on how news coverage affects crypto confirms this: sustained earned coverage compounds credibility in ways that single announcements cannot.

The Mainnet PR Sequence at a Glance

This table maps each PR activity to its timing relative to mainnet launch day.

Phase

When

Key action

Goal

Technical deep-dive

4 weeks before

Architecture explainer in developer outlets

Establish what the network does

Founder commentary

3 weeks before

Expert quotes on the trend of mainnet addresses

Position within a known narrative

Performance proof

2 weeks before

Testnet results, audit data, benchmarks

Create factual anchors for journalists

Embargo distribution

1 week before

Press kits to 5-8 journalists with visuals

Prepare coordinated coverage

Launch day

Day of

Simultaneous embargo lift, founder interview, community activation

Maximise coverage density

First-week metrics

Week 1-2 after

Transactions, wallets, dApps deployed

Prove the network works live

Thought leadership

Week 3-4 after

Founder analysis of what the mainnet revealed

Shift from product news to industry insight

Ecosystem coverage

Month 2-3 after

Partnerships, integrations, dApp stories

Compound search authority and AI citation

 

What Makes a Mainnet Story Editorially Strong

Journalists decide whether to cover a mainnet launch based on five factors.

Differentiation: What does this network do that others do not? "It solves a specific problem that no other network addresses" is a story. "It's faster" is not.

Verifiability: Can the journalist check the claims on a block explorer? On-chain proof separates real launches from vaporware.

Developer adoption signals: How many teams committed to building before the mainnet? Early ecosystem activity signals product-market fit.

Timing relevance: Does the launch connect to a broader trend like RWA infrastructure or cross-chain interoperability? Stories that fit existing editorial calendars get covered faster.

Founder credibility: Has the founder built visible authority through prior mainnet media coverage? Outset PR's guide on how to land crypto stories in tier-1 media explains how to structure pitches that answer these editorial questions before the journalist has to ask.

Conclusion

A mainnet launch is a credibility asset, not a one-day event. Most projects capture the first headline and nothing else.

The ones that get full value from it run a deliberate sequence: anticipation before launch, coordinated announcement density on the day, and sustained narrative after the network goes live. The technical milestone opens the door. Mainnet launch PR determines how far the project walks through it.

Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.

Media planning is treated as a structured process. In practice, it is anything but. Behind most media plans sits a mix of disconnected tools, partial metrics, and subjective judgment. Teams are expected to make high-stakes decisions about where to invest budget and attention, yet the inputs they rely on are inconsistent and often contradictory.

The Core Problem: Fragmentation at Every Step

Media planning requires answering a simple question: which outlets will deliver the intended outcome?

The difficulty lies in how that answer is constructed.

A typical workflow looks like this:

traffic data from one platform

SEO metrics from another

manual checks of editorial fit

scattered notes on past coverage

internal assumptions about “reputation”

None of these inputs are wrong. But they are not designed to work together.

Each metric reflects a different methodology, a different timeframe, and a different definition of performance. When combined, they do not form a coherent picture. They create noise.

As a result, media planning becomes an exercise in interpretation rather than analysis.

Why Metrics Don’t Align

The industry relies heavily on surface-level indicators such as traffic and domain authority. These metrics are easy to access and simple to compare. They are also insufficient.

Traffic does not indicate influence.Domain authority does not reflect engagement.Publication volume does not translate into visibility.

More importantly, these metrics rarely explain how an outlet behaves within the broader media ecosystem:

Does it get cited by other publications?

Does it drive syndication?

Does it appear in AI-generated answers?

Does it reach the right audience, or just a large one?

Without this context, teams are left comparing numbers that do not answer the actual question.

The Cost of Inconsistent Decisions

When inputs are fragmented, decisions become inconsistent.

Two teams can evaluate the same outlet and arrive at different conclusions. The same team can make different choices across campaigns without a clear rationale.

This leads to predictable outcomes:

budget allocated to outlets that do not deliver impact

overreliance on familiar or “safe” publications

missed opportunities in niche or high-influence media

difficulty explaining results or improving strategy

In other words, inefficiency is not accidental. It is built into the system.

Why Existing Tools Don’t Solve the Problem

Most PR and media tools are designed for execution:

media databases help you find contacts

outreach tools help you distribute content

monitoring platforms track coverage after publication

They support the workflow, but they do not improve the decision itself.

The critical step—evaluating and selecting media outlets before publication—remains underdeveloped. Teams are still expected to reconcile fragmented data manually and make judgment calls under uncertainty.

This is the gap in the current media planning stack.

Outset Media Index Introduces a Decision Layer

What is missing in media planning is a dedicated decision layer.

A system that sits before outreach and answers:

which outlets to prioritize

why they matter

what role they play in a campaign

how they compare to alternatives

This layer turns planning into a repeatable process rather than a subjective exercise.

Where Outset Media Index Fits

Outset Media Index was designed to address this exact gap. Instead of relying on fragmented inputs, it consolidates media data into a single analytical framework. Each outlet is analysed across more than 37 metrics, covering reach, engagement, syndication, and influence within the information flow.

This changes how decisions are made.

Teams can:

compare outlets side by side using normalized data

identify which publications drive visibility versus volume

understand how content is distributed beyond the original placement

align media choices with specific campaign goals

The result is not more data, but structured clarity.

Rather than interpreting conflicting signals, teams work with a consistent system that supports decision-making from the start.

From Guesswork to Strategy

Media planning will not improve by adding more tools or more metrics. It improves when the underlying structure changes.

Fragmentation leads to inconsistency.Inconsistency leads to inefficiency.

A unified, decision-focused approach removes both.

As media ecosystems become more complex—shaped by syndication networks, aggregators, and AI-driven distribution—the cost of poor planning increases. So does the value of getting the decision right before a campaign begins.

The shift is straightforward:

From scattered data → to structured analysisFrom intuition → to comparabilityFrom execution-first → to decision-first

That is where effective media planning starts.

Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.

DUBAI, United Arab Emirates, April 15, 2026 /PRNewswire/ -- What will it take to build a financial system that billions of people can trust — and barely notice?

That question set the tone for a fireside chat titled "Trust, Technology, and Transformation: Building the New Financial Platform for a Tokenized Economy", where Bybit Co-founder and CEO Ben Zhou took the stage at Paris Blockchain Week 2026 to outline a future where finance becomes more intelligent, more accessible, and ultimately, invisible.

Rather than focusing on price cycles or short-term trends, Zhou framed the industry's next chapter as a fundamental redesign of financial infrastructure — one driven by the convergence of artificial intelligence, programmable assets, and regulatory clarity.

From Interfaces to Intelligence: The Rise of Agentic Finance

Zhou challenged the conventional idea of how users interact with financial platforms. In the future, he suggested, users may not interact with platforms at all.

"We've introduced AI agent accounts that allow clients to create sub-accounts for AI to interact, execute strategies, and access market data," Zhou shared. "Agentic payments are becoming a major theme — and we're just at the beginning."

Instead of manually navigating markets, users can delegate tasks to AI agents — systems that interpret data, execute decisions, and optimize outcomes in real time. Today, these applications are largely focused on analytics and data access. Tomorrow, they may redefine execution itself.

The implication is profound: the interface disappears, and intelligence takes its place.

The Quiet Transformation of Finance

While much of the public narrative still centers on "crypto," Zhou pointed to a quieter, more consequential shift already underway.

Traditional financial institutions are not entering the space through speculation — they are integrating blockchain as infrastructure. Stablecoins, in particular, are emerging as the bridge, enabling faster payments, more efficient settlement, and global liquidity access.

In many cases, Zhou noted, these institutions are building on crypto rails without embracing the label itself.

This signals a turning point: crypto is no longer an alternative system — it is becoming part of the foundation.

Trust Is the Real Product

For Zhou, the defining constraint — and opportunity — is not technology, but trust.

"The regulatory framework has become significantly clearer in recent years. Jurisdictions like the UAE are setting the pace by actively welcoming innovation and providing structured pathways for growth."

From Europe's structured approach to the evolving stance in the United States and the United Kingdom, regulatory clarity is no longer a barrier — it is becoming a catalyst.

As rules solidify, institutions follow. And as institutions enter, the system begins to mature.

A System That Works Without Being Seen

Zhou closed with a perspective that reframed the industry's ultimate goal:

"This is not about replacing existing financial systems, but enhancing them. Our focus is on building infrastructure that makes financial services more accessible, efficient, and intuitive for users globally."

The end state, he suggested, is not a world where users think about blockchain, wallets, or even platforms — but one where financial services simply work, seamlessly embedded into everyday life.

In that future, trust is built into the system, intelligence operates in the background, and technology fades from view.

#Bybit / #TheCryptoArk / #NewFinancialPlatform

About Bybit

Bybit is the world's second-largest cryptocurrency exchange by trading volume, serving a global community of over 80 million users. Founded in 2018, Bybit is redefining openness in the decentralized world by creating a simpler, open and equal ecosystem for everyone. With a strong focus on Web3, Bybit partners strategically with leading blockchain protocols to provide robust infrastructure and drive on-chain innovation. Renowned for its secure custody, diverse marketplaces, intuitive user experience, and advanced blockchain tools, Bybit bridges the gap between TradFi and DeFi, empowering builders, creators, and enthusiasts to unlock the full potential of Web3. Discover the future of decentralized finance at Bybit.com.

For more details about Bybit, please visit Bybit Press

For media inquiries, please contact: media@bybit.com

For updates, please follow: Bybit's Communities and Social Media

 Facebook | Instagram | LinkedIn | Reddit | Telegram | TikTok | X | Youtube

 

Disclaimer: This is a sponsored press release and is for informational purposes only. It does not reflect the views of Bitzo, nor is it intended to be used as legal, tax, investment, or financial advice.

As we cross the mid-point of April 2026, the narrative of "crypto as money" is undergoing a high-tech facelift. The market's attention is pivoting toward high-throughput chains capable of handling global payment volumes without breaking a sweat. In this arena, Kaspa (KAS) and Toncoin (TON) stand out as the primary contenders, though they are currently running at very different speeds. While one is still warming up its engines at a support base, the other is already accelerating down the track.

Kaspa (KAS): Early Base, Not Yet Leadership

 

Source: tradingview 

Kaspa (KAS) is currently focused on the Toccata hard fork, which reached its critical "feature freeze" today, April 15, 2026, ahead of its scheduled June activation. This upgrade aims to transition the network from a pure "fast cash" DAG into a programmable smart contract platform with native ZK infrastructure and Covenants++. Despite the recent mainnet launch of the Igra Network (EVM layer) and WarpCore’s integration with traditional banking rails, KAS remains in a "neutral-to-weak" technical state. Trading just under its 7-day ($0.0325) and 30-day ($0.0339) moving averages, KAS is struggling to turn its high-throughput fundamentals into a definitive breakout.

Kaspa (KAS) Price Scenarios:

Base Case: A sideways consolidation within a -20% to +30% band (roughly $0.026–$0.042). The market is currently weighing the Toccata hard fork's potential utility against its June activation timeline, keeping the price in a defensive range.

Bullish Path: A speculative "Fast PoW" rally targeting $0.045–$0.05 (+35% to +55%). This would require a daily close above the 30-day SMA, likely fueled by a spike in developer interest as the "Covenants++" mainnet rehearsal begins.

Bearish Path: A failure to hold the current support base, leading to a slide toward $0.022–$0.025 (-25% to -35%). If macro sentiment turns risk-off, KAS may revisit its local lows before the new Layer-1 programmability kicks in.

Toncoin (TON): Stronger Trend, Higher Bar

Source: tradingview 

Toncoin (TON) is the current momentum favorite in the payments sector following the successful activation of Catchain 2.0 on April 9, 2026, which slashed block generation times to 400 milliseconds. This "MTONGA" (Make TON Great Again) upgrade has made Telegram-integrated payments effectively sub-second, a move that recently landed Toncoin on Grayscale’s Q2 Watchlist. While the network faces a temporary jump in inflation to 3.6% due to the faster block rate, the market is already pricing in a June vote to curb validator rewards. Trading firmly above its 7-day ($1.36) and 30-day ($1.28) averages, TON is the most likely candidate to lead a payments-layer rally, though it now faces the "boss level" resistance of its long-term average.

Toncoin (TON) Price Scenarios:

Base Case: A healthy consolidation within a -15% to +35% band (roughly $1.15–$1.85). TON is currently using its 30-day SMA ($1.28) as a durable springboard for further attempts at upper-range resistance.

Bullish Path: A leadership leg targeting the $1.68 200-day average (+25% to +40%). A push to this level would confirm a full trend reversal, potentially triggered by the next MTONGA milestone: a 6x reduction in transaction fees.

Bearish Path: A "priced-in" pullback toward $1.05–$1.10 (-20% to -25%). This is a realistic risk if messaging-payment headlines stall and speculative capital rotates into more deeply discounted "value" laggards.

Conclusion

As we move through Q2 2026, Toncoin (TON) is the clear frontrunner for the payments-layer narrative, backed by sub-second finality and the distribution power of Telegram. Kaspa (KAS) offers a compelling "value" alternative, but it must first prove that its upcoming Toccata upgrade can attract sustained on-chain volume.

If the high-throughput narrative survives the month, expect TON to maintain its leadership while KAS acts as a high-beta catch-up play once its reversal is confirmed. If headlines turn into noise, TON has the stronger cushion of support, while KAS remains more vulnerable to further range-bound drift.

 

 

Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.

The decentralized exchange (DEX) landscape in April 2026 is becoming a tale of two architectures. As cross-chain interoperability becomes the "holy grail" for liquidity and Solana continues its streak of high-velocity retail trading, two protocols have emerged as the primary proxies for these trends: THORChain and Jupiter. While the broader market watches Bitcoin’s dance around the $71,000 mark, the internal plumbing of DeFi is undergoing a significant stress test.

THORChain (RUNE): Early Basing After A Pullback

 

Source: tradingview 

THORChain (RUNE) is currently positioning itself as the "Monetary Base" of the cross-chain world. The big news driving sentiment this week is the imminent Monero (XMR) and Zcash (ZEC) mainnet integration, set for the end of April. This move into privacy-focused assets is a massive bid for "trustless" swaps that don't rely on bridges. However, despite hitting a $1 billion swap milestone recently, RUNE's price action is currently in a "wait and see" mode. Trading between its 7-day ($0.398) and 30-day ($0.406) moving averages, RUNE is showing early signs of momentum stabilization, but it remains heavily suppressed by its $0.61 long-term average.

RUNE Price Scenarios:

Base Case: A wide neutral band between $0.32 and $0.52 (-20% to +30%). Dips toward the $0.30s are likely to find buyers, but the 98% ATH drawdown acts as a heavy psychological lid for new retail capital.

Bullish Path: A cross-chain rotation targeting $0.55–$0.65 (+35% to +60%). This would be triggered by the successful Zcash integration and the rollout of Protocol-Owned Liquidity (POL), which should deepen pools and reduce slippage.

Bearish Path: A failure to hold the current base, leading to a slide toward $0.26–$0.30 (-25% to -35%). This remains a risk if capital continues to favor single-chain ecosystems over the complex "chain-agnostic" model RUNE offers.

Jupiter (JUP): Solana DEX Flow Proxy With A Healthier Trend

Source: tradingview 

Jupiter (JUP) is currently the "king of the hill" on Solana, commanding a staggering 95% share of the aggregator market. While the community is still buzzing about the Express Verification API launch on April 7—which allows for the programmatic verification of new tokens—the token's price action is largely being shaped by the Jupuary airdrop delay. The DAO recently voted to push the final 400M JUP distribution to May 2026, which has temporarily removed potential sell pressure from the market. Technically, JUP is in a much healthier position than RUNE, trading above both its 7-day ($0.164) and 30-day ($0.158) moving averages.

JUP Price Scenarios:

Base Case: A constructive uptrend within a $0.14 to $0.22 band (-15% to +30%). As long as Solana's PreStocks (tokenized assets) volume stays at record highs, JUP should find consistent demand.

Bullish Path: A Solana-led DeFi rotation targeting $0.23–$0.27 (+35% to +60%). This move targets the 200-day MA and would likely be driven by the expansion of the JupUSD stablecoin into its planned "third use case" later this quarter.

Bearish Path: A liquidity ceiling fade toward $0.11–$0.13 (-20% to -30%). If Solana's network activity cools significantly before the Alpenglow upgrade in Q2, JUP’s aggressive valuation multiple (currently ~8x revenue) might face a reset.

Conclusion

The internal battle in the DEX sector is clear: Jupiter has the momentum and the ecosystem "stickiness" on Solana, while THORChain offers a higher-risk "value" play based on its upcoming privacy coin integrations. In the near term, JUP is the more credible leader for a DEX rotation, especially given its cleaner technical profile. RUNE, meanwhile, remains a "show me" token that needs to translate its ambitious roadmap into durable on-chain volume to break out of its current base.

 

 

Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.

Kingstown, St. Vincent and the Grenadines, April 15th, 2026, Chainwire

Bitunix, a cryptocurrency derivatives exchange, announced that it has obtained ISO/IEC 27001:2022 certification, a widely recognized international standard for information security management given by the International Organization for Standardization (ISO).

The certification confirms that Bitunix exchange has established formal systems to manage and protect sensitive data, including user information and their assets. It follows an external audit process that evaluates how organizations identify risks, control access, and respond to potential security incidents.

With ISO 27001:2022 now achieved, for Bitunix users, the impact is practical. It means stronger protection of personal information and funds, better alignment with international data protection rules, and more transparency around how the platform operates. This also builds greater trust for users on the platform and, at the same time, the certification pushes the company to keep improving how it operates, from internal processes to overall platform stability. For users, that translates into a more reliable experience and a platform that is consistently working to perform better.

ISO 27001:2022 sets out clear requirements for how companies should organize their security practices, from internal procedures to technical safeguards. For exchanges, where large volumes of funds and personal data are handled, such standards are increasingly seen as essential rather than optional; hence, Bitunix achieved this certification.

A Continued Push Toward Stronger Security and Transparency

Known for high standards when it comes to security and transparency, alongside the certification, Bitunix exchange continues to build on its existing security setup through several practical measures reflecting ongoing efforts to improve how the company safeguards its platform and users.

The platform maintains proof of reserves showing more than 100% backing for BTC, ETH, and USDT, supported by real-time Merkle tree verification. It also applies a strict 1:1 asset backing model, ensuring that all user funds are fully matched. In addition, users are given access to open-source tools and a verification portal to independently check their balances.

To cover unexpected situations, Bitunix has also set aside a dedicated $30 million USDC care fund. Therefore, the ISO 27001:2022 certification adds to these efforts and reflects a broader push to keep improving how the exchange protects users.

The company said it will keep updating its systems as it grows, with a focus on keeping things safe and transparent for users.

“Achieving ISO/IEC 27001:2022 certification reflects our deep commitment to security and transparency,” said Steven Gu, Bitunix’s Chief Strategy Officer. “At Bitunix, we believe trust is earned through action. This certification, alongside our Proof of Reserve system, ensures our users can trade with confidence.”

Bitunix said it plans to continue updating its security practices as the platform expands and as threats evolve.

About Bitunix

Bitunix is a global cryptocurrency derivatives exchange trusted by over 5 million users across more than 150 countries. Guided by its core principle of better liquidity, better trading, the platform is built for traders who expect more, committed to providing Ultra Trust, Ultra Products, and Ultra Experience. Bitunix offers a fast registration process and a user-friendly verification system supported by mandatory KYC to ensure safety and compliance. With global standards of protection through Proof of Reserves (POR) and the Bitunix Care Fund, the exchange prioritizes user trust and fund security. Industry-first innovations like Fixed Risk, TradingView-powered chart suite, along with indicator alerts, cloud-synced templates, provide both beginners and advanced traders with a seamless experience. Making Bitunix one of the most dynamic platforms on the market.

Bitunix Global Accounts

X | Telegram Announcements | Telegram Global | CoinMarketCap | Instagram | Facebook | LinkedIn | Reddit | Medium

ContactCOOKx Wukx.wu@bitunix.io

Disclaimer: This is a sponsored press release and is for informational purposes only. It does not reflect the views of Bitzo, nor is it intended to be used as legal, tax, investment, or financial advice.

Crypto projects with limited budgets face the same resource question every quarter: spend on KOL campaigns for fast community reach or invest in earned PR for long-term credibility. 

The answer depends on timing, goals, and one critical difference most founders overlook. Influencer posts decay within 48 hours. Earned media compounds for months through search indexing, syndication, and AI citation. 

This article compares both channels across five dimensions: shelf life, trust signals, investor perception, AI visibility, and cost per lasting impression.

How Each Channel Works

Both channels produce visibility, but through entirely different mechanics and with different shelf lives attached to the output.

Influencer marketing (KOL campaigns)

A crypto project pays a Key Opinion Leader to create content about the product: tweets, YouTube videos, Telegram posts, X threads. The content reaches the KOL's audience immediately. Engagement peaks within 24 to 48 hours, then drops sharply.

The project has limited control over messaging. The KOL's personal style and audience expectations shape how the story is told.

According to the Consumer Insight’s Influencer Trust Index, 74% of consumers trust influencer recommendations, and crypto KOL vs PR decisions often hinge on this trust premium during launch windows.

Earned media (PR)

A PR agency pitches a story to a journalist, who decides whether to cover it based on editorial merit. The resulting article appears in a publication that the journalist's editor approved. It carries no "sponsored" or "paid" label.

The article remains indexed in search engines, gets syndicated across aggregators, and feeds into AI training data. A journalist chose to cover the project. 

This editorial selection is what investors and AI systems treat as independent validation. That distinction sits at the heart of earned media crypto strategy.

Outset PR explored this dynamic in its analysis of whether PR cuts marketing costs or drains the budget, showing that earned coverage reduces drop-off across every acquisition channel, including influencer. That distinction sits at the centre of earned media crypto strategy.

The Shelf-Life Gap: 48 Hours vs 12+ Months

The difference between these two channels becomes sharpest when you measure how long each piece of content continues to generate value after publication.

Influencer content half-life

Research published in the Proceedings of the AAAI Conference on Web and Social Media found that the median half-life of a tweet is roughly 80 minutes, and after 24 hours, no relevant number of impressions can be observed for roughly 95% of all tweets. 

An X thread peaks within four hours. A Telegram or Discord shoutout gets buried by new messages within hours.

After one week, the visibility value of a KOL post has largely expired. The audience has moved on to the next thing. When founders compare paid vs earned crypto visibility, this decay curve is the variable they underestimate most.

Earned media half-life

A CoinDesk or Cointelegraph article remains indexed in Google for months or years. Each article generates backlinks that build search authority over time. 

Syndication spreads the article to CoinMarketCap, Binance Square, Yahoo Finance, and Google News within hours of publication, and those republications stay indexed independently.

AI systems draw from published media when composing answers. An earned article placed today can appear in an AI-generated answer six months from now. 

Outset PR's research found that PR opens more doors in influencer outreach precisely because earned coverage creates the credibility layer that makes KOL partnerships more effective. The two channels reinforce each other when sequenced correctly.

How Investors and AI Systems Treat Each Channel

Credibility signals carry different weight depending on who is reading them. Two audiences matter most for crypto projects seeking long-term traction: venture capital investors and AI answer engines.

Investor perception

VCs run media due diligence before investing. Earned editorial coverage in tier-1 outlets signals independent validation. A Forbes article where the founder was interviewed carries more weight than 20 paid KOL posts.

Paid influencer content is visible to investors, too, but they discount it because they know it was purchased. The editorial selection signal is missing. 

A founder with consistently earned coverage across CoinDesk, Decrypt, and Business Insider looks fundamentally different in due diligence than one whose media presence consists entirely of KOL shoutouts. 

This is why crypto PR vs influencer marketing is not just a marketing question. It is a fundraising question as well.

AI system treatment

Large language models weight editorially selected content from high-authority publications more heavily than social media posts. An earned article in The Block feeds into AI training data and retrieval systems. A KOL tweet typically does not.

Projects with strong earned media footprints appear in AI-generated answers to category queries. Projects with only influencer coverage usually do not. 

Outset PR documented that AI referrals now account for 25.6% of referral traffic to US crypto media, confirming that the AI channel is already significant enough to factor into the influencer marketing ROI crypto calculation.

When to Use Each Channel

The right channel depends on the scenario, the timeline, and what the project needs to signal. Here is a breakdown by situation.

Scenario

Best channel

Why

Token launch needs immediate community awareness

Influencer

Speed and direct audience access in the 48-hour launch window

Pre-fundraise credibility building

Earned media

Investors verify through media due diligence, not KOL posts

Product launch to a crypto-native audience

Both

Earned media for credibility, influencer for distribution

Post-crisis reputation repair

Earned media

Editorial coverage rebuilds trust; paid content looks defensive

Community growth in a specific geo

Influencer

Local KOLs reach specific language and geo audiences faster than international media

Long-term brand authority and AI visibility

Earned media

Compounds through search, syndication, and AI training data

Exchange listing announcement

Both

Earned media for institutional confidence, influencer for retail excitement

How the Two Channels Reinforce Each Other

The most effective approach treats earned media and influencer marketing as sequential, not competing.

Earned media first. Place earned editorial coverage that establishes what the project does and why it matters. This creates the credibility foundation.

Influencer amplifies. KOLs reference or share the earned coverage with their audiences. A KOL pointing followers to a CoinDesk feature about the project carries more weight than a KOL delivering a paid script. The credibility transfers.

Earned media compounds. The initial coverage generates syndication, search authority, and AI citations. Each new earned placement builds on the last.

Outset PR's Press Office model produces the sustained earned coverage that makes influencer campaigns more effective. 

The Choise.ai campaign generated 2,729 republications at an average of 50 per article, creating a media density that gave every subsequent marketing channel, including influencer, a credibility boost.

Conclusion

Influencer marketing and earned media solve different problems on different timelines. Influencer posts deliver fast reach that decays within days. Earned media builds authority that compounds for months through search, syndication, and AI visibility. 

The strongest strategies sequence earned media first, then use influencer campaigns to amplify validated coverage. The question is not which channel is better. It is the sequence that matches the project's stage, goals, and budget.

 

 

Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.

Sindikācija agrāk nozīmēja kaut ko diezgan konkrētu. Stāsts tika pārrakstīts, saistīts un nosūtīja satiksmi atpakaļ uz avotu. 2026. gadā arvien lielāka daļa no "sindikācijas" notiek bez pārrakstīšanas vispār. AI vadīti plūsmas un LLM balstītas saskarnes saspiež informāciju ekrānā redzamā atbildē. Lielākā daļa lietotāju pārskata, iegūst to, kas viņiem nepieciešams, un turpina bez klikšķināšanas.

Šis maiņš maina izplatīšanas ekonomiku. Tas arī maina to, uz ko PR un redakcionālajām komandām būtu jākoncentrējas, jo uzvara var izskatīties kā citāts, parafrāze vai zīmola pieminēšana bez klikšķa.

2026. gada aprīļa vidū Layer-1 un Layer-2 sektoru "Vecā Gvardija"—Polygon un Polkadot—atrodas dīvainā tehniskā standoffā. Neskatoties uz virkne augstas ietekmes virsrakstu, tostarp veiksmīgu Polygon Giugliano hardfork aktivizāciju un Polkadot vēsturisko "Pusēšana" piegādes samazinājumu martā, abi aktīvi joprojām ir iesprostoti zem savām vairāku mēnešu tendencēm. Investoriem jautājums ir, vai šie pamatuzlabojumi veido izturīgu grīdu izlaušanās gadījumam, vai tirgus vienkārši "pārdod jaunumu" ilgstošā sānu griešanā.

As we move through mid-April 2026, the decentralized finance (DeFi) sector is witnessing a subtle but persistent uptick in activity. With stablecoin transaction volumes hitting new all-time highs and on-chain swap efficiency becoming a primary focus for institutional capital, the "blue-chip" protocols—Uniswap and Curve—are back in the spotlight. However, while the fundamental "pipes" of DeFi are as busy as ever, their native tokens, UNI and CRV, are currently locked in a battle against heavy multi-month resistance.

Uniswap (UNI): Liquidity Winner, Technically Still Mid‑Range

 

Source: tradingview 

The technical picture is one of early improvement rather than a clean trend reversal. While the 7-day SMA ($3.16) is finally supporting the current price, the 30-day ($3.43) and 200-day ($5.20) moving averages remain significant overhead obstacles. The MACD histogram (+0.0057) is turning up from weak levels, but until the MACD line itself crosses into positive territory, the momentum is best described as "bottom-fishing."

TradingView Watchlist: Watch for a daily close above the $3.43 (30-day SMA) level. A sustained break here, accompanied by an RSI-14 climb into the 55–65 band, would signal that the bulls are finally wrestling control back from the sellers.

Near-Term Scenario Map

Base Case (-15% to +25%): UNI continues to oscillate between $2.70 and $4.00. Continued DEX volume strength keeps the floor intact, but the 200-day MA likely caps any rallies without a massive volume surge.

Bullish Path (+30% to +50%): A genuine DeFi comeback pushes UNI toward $4.10–$4.75. This would require a confirmed "DeFi Summer 2.0" rotation and clearly positive MACD signals.

Bearish Path (-20% to -30%): If capital rotates into newer narratives like AI infrastructure or RWAs, UNI may drift toward $2.50–$2.20.

Curve (CRV): Slightly Better Short‑Term Setup, Still Under Heavy Lid

Source: tradingview 

CRV’s indicators are marginally more constructive. The MACD histogram (+0.0016) is rising, and the RSI-7 (55.1) is nudging into bullish territory. While the price ($0.2169) is still under the 30-day ($0.222) and 200-day ($0.38) SMAs, the tightening of the shorter-term averages suggests a volatility expansion—likely to the upside—could be imminent if stablecoin flows persist.

Near-Term Scenario Map

Base Case (-15% to +30%): CRV trades in a band between $0.18 and $0.28. It likely outperforms UNI on high-volume swap days due to its tighter liquidity and specific yield-farming flows.

Bullish Path (+35% to +60%): A rotation led by stablecoin rails pushes CRV toward $0.29–$0.35. Breaking the 30-day MA with volume is the key trigger for this move.

Bearish Path (-20% to -35%): Governance concerns or shifting incentive programs could lead to a slide toward $0.17–$0.14 if the current support at $0.21 fails to hold.

Conclusion

The data confirms that both UNI and CRV are currently "survivors" rather than "leaders." Their structural trends remain bearish as they trade well under their 200-day moving averages. However, the MACD and RSI profiles suggest a tentative floor is being built.

If DEX and stablecoin activity remain at their current elevated levels through Q2 2026, we may see these blue chips re-rate by 30–50% as capital seeks the safety of established protocols. Until then, expect a wide-range grind where rallies are sold into until the long-term averages are convincingly reclaimed.

 

 

Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.

AI referrals already account for 25.6% of all referral traffic to US crypto-native media. Outset PR has tracked this shift across successive quarters and identified it as one of the most significant structural changes in how crypto brands get discovered.

That share grows every quarter, and the brands capturing it are not necessarily the ones with the most coverage. 

They are the ones whose coverage appears in the right places, in the right format, with consistent language across sources.

AI search crypto PR operates on different inputs and different rules than search engine ranking. 

Less than 15% of crypto projects have taken meaningful steps to appear in AI-generated answers, and the gap between who AI recommends and who deserves to be recommended widens every quarter. This article explains the mechanism and what PR content triggers it.

How AI Systems Decide Which Brands to Name

Three layers determine whether a crypto project surfaces in an AI-generated answer. Miss any one of them and the project disappears from AI discovery entirely.

Layer 1: Training Data

LLMs are trained on large volumes of text from the open web, and not all sources carry equal weight. Publications with strong editorial standards, such as CoinDesk, The Block, Decrypt, Cointelegraph, Forbes, and Bloomberg, contribute disproportionately to what a model knows. 

A project with five earned editorial features across those outlets has a fundamentally different footprint in training data than one with fifty paid placements on low-authority sites. This is why earned media matters more for the LLM brand visibility in crypto than paid coverage does.

Layer 2: Real-Time Retrieval

Tools like Perplexity, Google AI Overviews, and ChatGPT with browsing access pull fresh content from the web when answering queries. This layer rewards recency and publication authority simultaneously. 

Coverage in CoinDesk this week outweighs coverage six months ago on a low-traffic outlet. Outset PR's own research found that AI referrals now account for 25.6% of all referral traffic to US crypto-native media. This is already a primary discovery channel, not an emerging one.

Layer 3: Entity Recognition and Narrative Consistency

AI systems perform best when they can unambiguously identify what a brand is and what it does. If coverage describes a project as a "DeFi protocol" in one outlet, a "yield platform" in another, and a "tokenised fund" in a third, the model struggles to form a stable association. 

Narrative consistency across publications directly increases the probability that an AI selects a brand when answering a category query. This layer is the one most projects ignore entirely.

What PR Content Triggers AI Citations

Not all coverage feeds AI Web3 discovery equally. Format, structure, and placement location all determine whether an AI system picks up a piece of content. The table below maps each content type to its AI citation impact and the mechanism behind it.

Content type

AI citation impact

Why

Earned editorial in tier-1 outlets

High

Models weight editorially selected content over advertising

Structured content with data and named methodologies

High

LLMs prioritise specific facts and clear formatting

Consistent brand descriptions across sources

High

Reduces entity ambiguity, strengthens model association

Reactive commentary in trending articles

Medium

Associates the brand with topics AI is actively indexing

Sponsored or partner content

Low

Models distinguish editorial from paid placement

Community channels (Discord, Telegram, X)

Minimal

Not indexed by AI retrieval systems

Distributing content across multiple trusted publications canincrease AI citations by up to 325% compared to publishing only on a brand's own site. 

Outset PR applied this directly by defining "data-driven crypto PR" as a category and maintaining that language across every publication, blog post, and media contribution to build a stable entity profile. 

Reactive commentary contributes to AIO crypto PR in ways most teams do not anticipate: when a founder appears as a named expert source in a breaking-news article on a topic AI models are indexing, the brand gets associated with that topic in the model's context.

Why Most Crypto Projects Are Invisible to AI

The editorial deficit is the root cause. A launch announcement on CoinMarketCap and a press release through a wire service do not build the footprint AI models draw from. 

Most crypto projects have never pursued serious earned media, which means they simply do not exist in the sources that LLMs treat as reliable.

Paid placements marked "sponsored" carry a lower weight in training data because models learn to distinguish editorial from advertising. A project with 100 paid placements and zero earned coverage will almost certainly be invisible in AI-generated category answers.

Community channels add another layer of confusion here. Discord, Telegram, and X drive real human engagement, but those conversations are not indexed by AI retrieval systems. 

Reddit is the notable exception, accounting for roughly 47% of Perplexity's citations. Projects with strong communities but weak media footprints get discovered by humans and missed by AI.

How Outset PR Engineers AI Visibility

Outset PR is a crypto PR agencies that recognizes the importance of AI Optimisation (AIO) as a core service, and applied the methodology to itself before offering it to clients. The approach runs in three steps.

Entity definition first. Before any content goes out, the agency checks whether AI systems can unambiguously identify the brand. Shared names with other entities, inconsistent descriptions, and weak source coverage all create ambiguity that undermines every subsequent step.

Category ownership second. Rather than competing in broad terms, Outset PR defined a narrower category, "data-driven crypto PR," and built consistent content around that definition across its blog, case studies, and media contributions. 

The Crypto Daily case study documenting this process shows how entity-to-category positioning creates the kind of stable AI association that broad positioning never achieves.

LLM seeding third. Using syndication tracking, the agency identifies which publications AI models cite most frequently for relevant queries and prioritises placements in those outlets. 

Each piece is structured for AI retrieval: clear formatting, specific facts, direct answers, and consistent brand language throughout. 

The full rationale for this approach, and why it has become a competitive requirement rather than an optional upgrade, is set out in Outset PR's research on AI visibility and who stays relevant in crypto.

Conclusion

GEO crypto and AI discovery Web3 are not future concerns. AI referrals already account for more than a quarter of referral traffic to US crypto media, and that share grows every quarter. 

The projects that build an editorial footprint now, in the right outlets, with consistent brand language, are the ones that AI systems will surface when a VC associate, journalist, or potential user asks a category question six months from now.  The ones that wait are training AI to recommend someone else.

 

 

Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.

PHILADELPHIA, April 13, 2026 /PRNewswire/ -- The following statement is being issued by Kroll Settlement Administration on behalf of the United States Department of Justice regarding the OneCoin Cryptocurrency Remission Program ("Remission Program").

What is this about?

The Department of Justice has commenced a petition for remission process to compensate fraud victims who invested in the fraudulent cryptocurrency platform, OneCoin, between 2014 and 2019. The United States Attorney's Office for the Southern District of New York filed a number of OneCoin-related prosecutions in the Southern District of New York.

Between 2014 and 2019, Ruja Ignatova and Karl Sebastian Greenwood, co-founders of OneCoin Ltd., and others, orchestrated a large, international cryptocurrency investment scheme defrauding investors from around the globe. The scheme involved the marketing and sale of fraudulent cryptocurrency, resulting in significant financial losses for victims worldwide. The United States Attorney's Office in the Southern District of New York pursued criminal forfeiture of proceeds of the fraud scheme and the net proceeds of those forfeited assets will be available to compensate victims through the remission process. Victims affected by the OneCoin scheme may file petitions for remission to receive compensation.

Who is eligible for compensation?

Victims who purchased OneCoin cryptocurrency between 2014 and 2019 and experienced a net loss of the investment when accounting for any completed withdrawals or collateral recoveries may be eligible to receive compensation in this matter. However, submission of a petition for remission does not guarantee payment. Neither the Department of Justice nor the Remission Administrator charge fees for you to file a petition or to participate in the remission process. Additionally, you do not need an attorney to file a petition.

What options do victims have?

Submit a Petition Form by June 30, 2026: To participate in this Remission Program, you must submit a completed petition form. As part of your submission, you will be asked to verify monetary losses that were incurred as a result of the scheme. Documentation to support all claimed losses must be included with the submission of your petition form. Petitions for remission can be submitted by mail or online on www.onecoinremission.com.

Do Nothing: If you do not wish to participate in the Remission Program, you do not need to file a petition form. No further action is necessary. If you do not submit a petition for remission, you will not be considered in the Remission Program.

Get More Information

This is only a summary. More details about the petition for remission process and instructions on how to submit a petition are available as follows:

Visit: www.onecoinremission.com

Call: 1-833-421-9748

Email: info@OneCoinRemission.com

Write: OneCoin Remission, c/o Kroll Settlement Administration LLC, P.O. Box 225391, New York, NY 10150-5391

Disclaimer: This is a sponsored press release and is for informational purposes only. It does not reflect the views of Bitzo, nor is it intended to be used as legal, tax, investment, or financial advice.

Content syndication used to be treated as an afterthought—an added benefit if a story happened to be republished elsewhere. That framing no longer holds. In 2026, syndication has become a structural component of media visibility, shaped as much by algorithms and network dynamics as by editorial intent.

What content syndication means today

At its core, content syndication still describes the distribution of content beyond its original publication. What has changed is the mechanism. A single article now moves through a layered system: direct republication, editorial referencing, algorithmic extraction, and AI-driven redistribution. The result is not a linear flow of exposure, but a networked process in which visibility is continuously redefined.

The three types of syndication

1. Direct syndication

This is the traditional model:

a publication republishes content in full or in part

agreements are explicit (e.g., partnerships, contributor networks)

Control is relatively high. Distribution paths are predictable.

2. Partner syndication

This operates through semi-structured relationships:

editorial collaborations

citation patterns between outlets

industry-specific media clusters

Content is not always republished in full. It is often:

summarized

referenced

embedded into broader narratives

Here, distribution depends on editorial behavior and network positioning.

3. Algorithmic syndication

This is the defining layer in 2026.

Content is redistributed by:

news aggregators

search engines

recommendation systems

LLMs and AI feeds

There is no direct agreement between publisher and distributor. Instead, algorithms decide what gets surfaced, how often, and in what format. This last layer has fundamentally changed how visibility works. Publications are no longer just endpoints for readership; they function as source nodes within a wider information system. Their output feeds into AI-generated answers, curated news feeds, and secondary publications. In many cases, influence now manifests without direct traffic. A piece can shape narratives, inform summaries, or be cited across platforms without users ever visiting the original source.

Why syndication is no longer linear

The old model was sequential:

publish → distribute → measure

The current model is networked:

publish → propagate across multiple paths simultaneously

Content can:

move laterally across peer publications

resurface weeks later through algorithmic systems

gain visibility without direct attribution

Distribution paths overlap and reinforce each other. There is no single “channel” to track.

What shapes syndication today

What determines how far content travels within this system is not a single metric, but a combination of structural factors. Media relationships still matter, particularly for direct and partner syndication. Editorial practices play a defining role, distinguishing outlets that originate narratives from those that amplify them. Increasingly, however, algorithmic systems act as the primary gatekeepers, deciding what is surfaced, prioritized, and reused across digital environments.

The difficulty is that most teams lack the tools to evaluate these dynamics. Standard metrics—traffic, domain authority, reach—capture only a fraction of what syndication represents today. They do not account for how content is redistributed, how often it is cited, or whether it appears in AI-generated outputs. As a result, syndication remains largely invisible at the point where it matters most: before a media decision is made.

This is where the concept of syndication depth becomes critical. Rather than focusing on immediate audience size, it measures how extensively content propagates across the media ecosystem. That includes reprints, citations, presence in aggregators, and visibility within AI systems. It is a structural indicator of influence, not just exposure.

Measuring Syndication Depth with Outset Media Index

Outset Media Index (OMI) is built around this shift. By consolidating fragmented signals into a unified analytical framework, it allows media teams to analyse outlets across multiple dimensions, including reach, engagement, LLM visibility, and syndication depth. The platform relies on a standardized system of over 37 metrics to provide a consistent basis for comparison and decision-making. Instead of interpreting conflicting data points in isolation, teams can assess how a publication performs within the broader information network.

The practical implication is straightforward. Media selection is no longer just about where content appears first. It is about where content travels. Choosing an outlet now means choosing a distribution profile: how content will be picked up, where it will resurface, and whether it will contribute to ongoing narratives.

Syndication, in this sense, is no longer incidental. It is engineered. Visibility is shaped by systems—editorial, relational, and algorithmic—and those systems can be analyzed. The advantage shifts to teams that treat distribution as a design problem rather than a post-publication outcome.

The industry has spent years optimizing for placement. The next phase is optimizing for propagation.

Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.

Kad 2026. gada aprīļa vidū tirgus attīstās, "Move-VM" naratīvs, kas centrējas ap Aptos un Sui augstas veiktspējas izpildes vidēm, saņem jaunu likviditātes injekciju. Ar jaunu Tier-1 CEX sarakstu un sarežģītu mūžīgo pāru vilni, spekulatīvās izsistīšanas infrastruktūra ir oficiāli sagatavota. Tomēr lentā tiek stāstīta piesardzības stāsts: kamēr likviditāte ir uzlabojusies, tehniskās struktūras joprojām ir iesprūdušas pēcpārtrauktā grindā. Investoriem tagad ir jāizlemj, vai šie ķēdes patiešām pagriežas uz labu pusi vai vienkārši nodrošina labākas izejas iesprūdušiem garajiem.

Kad mēs virzāmies uz 2026. gada aprīļa vidu, "Uzņēmumu tokenizācijas" stāsts atkal sāk dzirkstīt. Augsta profila izmēģinājumi, kas saistīti ar reālo aktīvu (RWA) emisiju un korporatīvās piegādes ķēdes izsekošanu, nonāk virsrakstos, liekot Hedera (HBAR) un MultiversX (EGLD) atkal nonākt uzmanības centrā. Tomēr, neskatoties uz fundamentālo troksni, abi aktīvi paliek iestrēguši nepārtrauktā lejupslīdē. Investoriem jautājums ir, vai šie institucionālā līmeņa L1 ir beidzot sagatavojušies jauna novērtējuma veikšanai, pamatojoties uz reālu pieņemšanu, vai arī šie virsraksti atkal tiks pārdoti diapazona izzušanā.

DALLAS, April 13, 2026 /PRNewswire/ -- Cango Inc. (NYSE: CANG) ("Cango" or the "Company"), a leading Bitcoin miner leveraging its global operations to develop an integrated energy and AI compute platform, today announced the launch of the official digital portal for its subsidiary, EcoHash Technology LLC ('EcoHash' or the 'Subsidiary'). Accessible at www.ecohash.com, this platform serves as the primary interface for EcoHash's high-performance computing (HPC) and AI inference operations. The site is designed to streamline strategic engagement with two key audiences: AI developers seeking low-latency, near-source compute, and energy-intensive compute operators pursuing modular pathways to infrastructure diversification.

Goldman Sachs Research forecasts that U.S. data center power demand could reach 700 TWh by 2030, largely driven by AI inference workloads, yet the maximum available supply remains just above 300 TWh, underscoring a structural gap of roughly 400TWH between soaring compute demand and delayed infrastructure deployment. EcoHash addresses these challenges by leveraging Cango's global energy footprint to deploy standardized, plug-and-play compute modules, paired with its proprietary EcoLink Orchestration Platform. This integrated system unifies and schedules geographically dispersed compute capacity to deliver enterprise-grade uptime through intelligent failover. The result: elastic, low-latency compute that scales seamlessly and activates on demand.

Cango is dedicating space at its owned 50MW Georgia mining facility to this initiative. By utilizing the facility's existing infrastructure and energy access, the site will operate full-series container models as a "living showroom". This facility is designed not only to demonstrate real-world performance across varying thermal and power configurations but also to serve as a strategic proof-of-concept hub for industry collaborators across the digital infrastructure and mining ecosystem. By showcasing the commercial viability of these plug-and-play modules, Cango aims to invite global partners to integrate into the EcoHash network. This collaborative approach aims to build a robust, globally distributed AI power grid, replicating the Georgia model across high-potential sites both within and beyond Cango's current network.

Jack Jin, Chief Technology Officer of EcoHash, commented, "EcoHash represents the core vehicle of our strategy to architect a future-ready platform and serve as our next growth engine, now entering a phase of accelerated commercialization. Our proprietary orchestration layer, the central nervous system of our network, is built to enable intelligent, real-time resource allocation. This connects decentralized energy assets directly to the demands of LLM inference, generative AI, and a growing spectrum of compute-intensive applications as our node infrastructure scales."

Contact: ir@cangoonline.com

Disclaimer: This is a sponsored press release and is for informational purposes only. It does not reflect the views of Bitzo, nor is it intended to be used as legal, tax, investment, or financial advice.

Editorial strategy has traditionally relied on experience, instinct, and partial signals. That approach breaks down in a fragmented media environment where audience behavior, distribution patterns, and influence dynamics shift continuously.

A data-driven editorial strategy replaces intuition with structured analysis. It allows teams to make decisions based on measurable signals—what performs, what spreads, and what shapes the narrative.

Why Intuition-Driven Editorial Planning Falls Short

Editorial teams often operate with incomplete visibility. Common inputs include:

traffic estimates

SEO indicators

anecdotal audience feedback

competitor observation

These signals are useful but isolated. They do not explain how content performs within the broader media ecosystem.

The result is predictable:

content that attracts clicks but lacks downstream impact

misalignment between editorial output and business goals

inefficient allocation of resources

The core issue is fragmentation. Data exists, but it is not structured into a system that supports decisions.

What Defines a Data-Driven Editorial Strategy

A data-driven approach does not replace editorial judgment. It refines it by grounding decisions in consistent signals.

At a practical level, this means:

1. Defining measurable outcomes

Editorial teams move from vague goals (“increase visibility”) to specific targets:

engagement depth

syndication potential

citation frequency

audience quality

2. Using multi-dimensional analysis

Single metrics distort reality. Traffic alone does not indicate influence, and publication volume does not reflect impact.

A structured approach evaluates multiple dimensions simultaneously:

reach (who sees the content)

engagement (how they interact)

distribution (how content spreads)

influence (how narratives propagate)

Outset Media Index (OMI) is a media intelligence platform that operationalizes this by analysing outlets across more than 37 normalized metrics, creating a comparable view of performance across publications .

3. Benchmarking performance within context

Performance only makes sense relative to the ecosystem.

Editorial teams need to answer:

How does this topic perform across competing outlets?

Which publications amplify similar narratives?

Where does influence concentrate?

A benchmarking framework provides these answers by placing each signal within a comparable structure.

The Role of Media Analytics Platforms

Editorial teams need infrastructure, not just data. This is where media analytics platforms become critical.

A structured platform consolidates fragmented inputs into a unified system, enabling direct comparison and decision-making.

Outset Media Index (OMI) addresses this by:

aggregating traffic, engagement, SEO/AIO, and editorial indicators

standardizing them into a single analytical framework

enabling side-by-side comparison of media outlets

Instead of switching between tools and reconciling conflicting metrics, teams work within one system that reflects how outlets actually perform .

This shift is operational, not theoretical. It reduces research time and removes ambiguity in editorial planning.

From Metrics to Editorial Decisions

Data becomes useful only when it informs action. A data-driven editorial strategy translates analysis into concrete decisions.

Topic Selection

Identify themes that:

generate sustained engagement

are picked up by other outlets

align with audience behavior trends

Outset Data Pulse supports this layer by interpreting how signals evolve over time, revealing patterns rather than snapshots .

Format and Depth

Determine whether the ecosystem favors:

short-form updates

long-form analysis

opinion-driven narratives

This is visible through engagement patterns and citation behavior.

Distribution Strategy

Select publication channels based on:

syndication depth

audience overlap

influence within the information flow

Some outlets generate reach; others shape narratives. The distinction is measurable.

Resource Allocation

Prioritize editorial effort where it produces:

measurable visibility

downstream amplification

strategic positioning

This replaces volume-driven publishing with targeted output.

Building an Editorial System, Not a Content Calendar

A data-driven strategy reframes editorial planning as a system.

Instead of asking “What should we publish next?”, teams ask:

What signals indicate opportunity?

Where does influence accumulate?

Which outputs align with measurable outcomes?

OMI functions as a decision layer in this system. It transforms scattered signals into a structured dataset that supports planning, benchmarking, and optimization .

Key Capabilities of Editorial Planning Tools

Effective editorial planning tools share several characteristics:

Unified data: multiple signals consolidated into one framework

Comparability: normalized metrics across outlets

Contextual insight: interpretation of trends, not just raw numbers

Actionability: outputs that inform concrete decisions

Without these, analytics remain descriptive rather than operational.

Conclusion

Editorial strategy is no longer a creative exercise supported by occasional data checks. It is an analytical process where content decisions are derived from structured signals.

The shift is clear:

from isolated metrics to unified frameworks

from intuition to benchmarking

from activity to measurable impact

Teams that adopt this model gain consistency, clarity, and control over how their content performs within the media ecosystem.

Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.