Foreword — by 
My 10 year anniversary on this blockchain is coming up soon, on the 13th of July this year. How many of us remember the buzz of the early years of this chain? The excitement and the optimism? There were times when Steem appeared to be on the verge of going mainstream. Hive has never even been a blip on the radar.
Even today, more people are landing on the now dormant platform that is steemit.com through search traffic from the legacy of those days than any of our front ends (even if you combine them together). Hive has so much more effort, so much more energy and work going into it than Steem or even some of the major cryptos, and yet nothing ever seems to move the needle.
Today, I am changing pace and presenting something more of a polemic supported by data than a research post. Alongside this, I am posting a DHF proposal. We need to fix what was accidentally broken when we forked from Steem.
The Case For A Rebrand — by Claude Opus 4.6
This analysis was conducted at
This is not a vague feeling that "Hive" is a bad brand. It is a measurable, testable, falsifiable claim: the name "Hive" breaks the mechanism by which token price should translate into new user acquisition, and without that mechanism, the platform cannot escape its decline.
The Flywheel
In an earlier post, we established that Hive's growth operates as a flywheel with two measurable links:
Link 1: Price drives acquisition. When the token price rises, more new users arrive and stay. A 10% price increase associates with ~3.1% more retained users arriving over the following 1–3 months (p = 0.017).
Link 2: Retained users drive price. Users who join and stay predict higher prices 4 months later. A 10% increase in retained users associates with ~10% higher price (elasticity ≈ 1.0, p = 0.04 in the Hive era; p < 0.001 across full chain history).
Critically, retention rate is independent of price (p > 0.27). Whether HIVE is at $0.90 or $0.15, the same proportion of newcomers stay. What changes is volume — how many people walk through the door.
This means the flywheel is mechanically simple: more users in → more retained → higher price → more users in. The total amplification per cycle is approximately 1.45×. A flywheel that amplifies growth also amplifies decline.
But the flywheel has a hidden dependency. For price increases to drive acquisition, people who notice the price pump need a way to find the platform. On the internet, that means search.
How the Flywheel Found Users on Steem
From 2016 to 2020, when this blockchain was called Steem, the pipeline was simple: price goes up → people Google "Steem" → they find steemit.com → they sign up. The word "Steem" was invented. No other product, company, or concept used it. Every search led to the blockchain.
We can measure this directly. Google Trends tracks relative search interest over time. Comparing weekly Steem search interest with the STEEM token price over the Steem era:
Pearson correlation: r = 0.86, p < 0.001 (48 monthly observations)
Price and search interest moved in near-lockstep. When the price spiked during the 2017-18 bull run, Google searches for "Steem" spiked with it. Those searches led to steemit.com — the only result — and monthly account creation peaked at 159,000 in January 2018.
The discovery pipeline worked because the name was unique.
How the Pipeline Broke
After the 2020 fork, the same blockchain became "Hive" — a common English word already claimed by:
| Google result | What it is |
|---|---|
| hive.com | Project management software (funded SaaS) |
| Apache Hive | Data warehouse (Apache Foundation) |
| thehive.ai | AI content moderation |
| hivehome.com | Smart home technology |
| HIVE Digital Technologies | A crypto miner (not the blockchain) |
The Hive blockchain does not appear on the first page of Google results for its own name. A person who sees HIVE pumping on an exchange and types "Hive" into Google will find project management software and a horror movie. They will never find us.
The same correlation analysis on the Hive era:
Pearson correlation: r = −0.22, p = 0.14, not statistically significant (49 monthly observations)
The correlation didn't just weaken. It collapsed to statistical noise. Price movements have zero ability to drive search discovery for the Hive blockchain.
| Era | Price vs search interest | Significance |
|---|---|---|
| Steem (2016–2020) | r = 0.86 | p < 0.001 |
| Hive (2020–2025) | r = −0.22 | p = 0.14, not significant |
Same blockchain. Same technology. Same community. Different name. The flywheel's intake valve was removed.
Proof That This Is the Name, Not the Market
A skeptic might argue: "Maybe the entire crypto market changed. Maybe search-driven discovery is dead everywhere." The data says no. We computed the same price-vs-search-interest correlation for four other tokens over the same 2022–2025 period, using their bare names on Google Trends and daily prices from CryptoCompare:
| Token | Name type | Price vs search r | Significant? |
|---|---|---|---|
| Solana | Unique | 0.72 | p < 0.001 |
| Polkadot | Distinctive | 0.58 | p < 0.001 |
| Avalanche | Generic (improving) | 0.11 | Not significant |
| Cosmos | Generic | −0.04 | Not significant |
| Hive | Generic | −0.17 | Not significant |
The pattern is a gradient. The more unique the name, the stronger the flywheel link. Solana — an invented word — maintains a strong price-to-search correlation every single year (r = 0.63 to 0.86, always p < 0.001). Polkadot — distinctive enough that crypto dominates the search results — holds similarly. Avalanche and Cosmos — generic words with heavy competition — show the same broken link that Hive does.
Year-by-year, Solana's correlation never drops below 0.63. Hive's never rises above 0.22 and is never significant. This is not a fluke — it is structural.
The difference between Hive and the other generic-name tokens is that Hive needs this pipeline more than any of them — and has the worst discoverability of them all. Avalanche and Cosmos are DeFi infrastructure and L1 platforms whose users are largely crypto-native — they find them through exchanges, aggregators, and developer docs. Hive is a social media platform. Its growth depends on buzz — the kind that happens when a token pumps, when someone mentions the platform in a podcast, when a curious person sees the name on an exchange. On Steem, that buzz converted: people heard the name, searched for it, and found the platform. On Hive, the same buzz dissipates into noise. Of the 50 largest cryptocurrencies, none is a consumer-facing product with an unsearchable name like Hive.
Quantifying the Noise
Google Trends lets us measure how invisible Hive is relative to comparable tokens. We computed a "crypto/bare ratio" — how much crypto-specific search volume each token captures relative to the noise in its generic name:
| Token | Crypto/Bare Ratio | Interpretation |
|---|---|---|
| Polkadot | 3.19 | Crypto has captured the word |
| Solana | 2.57 | Crypto dominates the search term |
| Avalanche | 0.58 | Competitive but findable |
| Cosmos | 0.30 | Struggling, but 5× better than Hive |
| Hive | 0.06 | Crypto is invisible in the noise |
Hive's ratio is 43× worse than Solana's. Approximately 91% of "Hive" searches have nothing to do with the blockchain. When someone sees HIVE on CoinMarketCap and Googles it, they enter a search space optimised for the other 9 out of 10 searchers — and the results confirm it. Not one of Google's "related searches" for "Hive" points toward blockchain, crypto, or social media. The search engine actively directs people away from us.
Among the top 50 cryptocurrencies by market cap, 38 have no discoverability problem at all — they chose invented or sufficiently distinctive names. Only 3 tokens share Hive's severity of search invisibility (Rain, Mantle, Sky), and none of them are consumer platforms that depend on casual search discovery.
The Traffic Deficit
The search invisibility is not abstract — it shows up in web analytics. SimilarWeb and Semrush data (2025–2026) paint the picture:
| Site | Monthly visits | Primary traffic source |
|---|---|---|
| steemit.com | ~2.9M | 67% organic search |
| peakd.com | ~510K | Direct / community |
| ecency.com | ~212K | Direct / community |
| hive.blog | <200K | Direct / community |
| hive.io | Declining 3.8% MoM | Direct |
| All Hive frontends combined | <1M | Negligible organic search |
Steemit — the old Steem frontend, running on older technology with no active curation programs — still draws 3× more traffic than all of these Hive frontends combined. The difference is organic search. Two-thirds of Steemit's visitors arrive via Google. Steemit built its search presence during the years when the pipeline worked — when "Steem" was an unambiguous search term and millions of users were creating content. That SEO legacy still compounds today. Hive never had the chance to build one.
Hive's frontends survive on direct traffic — existing users who already know the URL. Almost nothing arrives via search. The top of the funnel is dry.
On-chain monthly active users confirm the trajectory:
| Frontend | Jul 2025 | Jan 2026 | Change |
|---|---|---|---|
| PeakD | 3,900 | 3,100 | −21% |
| Ecency | 3,200 | 2,200 | −31% |
| Hive.blog | 1,400 | 1,000 | −29% |
| InLeo | 1,300 | 700 | −46% |
(Source: 
Every frontend is shrinking. Not because the apps got worse — PeakD is arguably the best blockchain blogging interface ever built — but because nobody new can find them.
The Wasted Effort
This is the point where the abstract data connects to lived experience. Consider what the Hive community has built and invested in:
- Curation programs like Aliento that demonstrably improve retention from 19% to 46%
- Frontend development — PeakD, Ecency, LeoFinance, 3Speak, Liketu
- Marketing campaigns and awareness initiatives funded by the DHF
- Community building across dozens of language and interest groups
- Infrastructure — witness nodes, API servers, developer tooling
All of this work is real and much of it is effective at the individual level. Aliento's retention numbers prove that dedicated curation works. PeakD's switching advantage proves that better apps matter. The reply speed data proves that engagement retains newcomers.
But none of it compounds. Every improvement operates on a shrinking base of users because the top of the funnel — the mechanism by which price should translate into new arrivals — is broken. You can double the retention rate and it doesn't matter if the number walking through the door halves at the same time.
This is the frustration that everyone who has built something on Hive has felt. The effort goes in. The needle doesn't move. And the reason it doesn't move is that the flywheel's intake valve is jammed by a name that search engines don't connect to a blockchain.
The Splinterlands Test Case
"But Hive did get massive user growth — 276,000 accounts in September 2021 alone."
True. And this example proves the point more than it refutes it.
The Splinterlands boom happened because Splinterlands had its own discoverable brand. People Googled "Splinterlands," found the game, and signed up. The discovery loop was entirely separate from HIVE's price — the signup surge began in July 2021 at $0.33 HIVE, three months after the price peak, and was driven by play-to-earn virality, not token appreciation.
The results:
- 868,000 accounts created during the boom (July–December 2021)
- ~34,000 ever made a blog post — roughly 4%
- The vast majority were gaming accounts that never touched the social platform
Those 868,000 users had Hive accounts. But they were never Hive users. They discovered Splinterlands, not Hive. They had no reason to explore the broader ecosystem because they didn't know it existed — and if they'd tried to learn about "Hive," they would have found project management software.
When Splinterlands' momentum faded, most of those users left — with little cross-pollination into PeakD, 3Speak, or other Hive apps. The blockchain was invisible infrastructure they passed through on the way to a game.
A discoverable blockchain name would have changed this. If "Hive" were a unique, searchable term, some fraction of 868,000 gaming users would have Googled the blockchain behind their game and found a social platform, a blogging community, a financial ecosystem. Even a 5% conversion rate would have meant 43,000 new ecosystem users — more than the entire current monthly active user base.
Instead: zero.
Quantifying the Damage
The correlation data shows the pipeline is broken. But how many users has Hive actually lost?
We can answer this directly. In the Steem era, the relationship between token price and monthly signups was strong and stable: a log-log regression over 48 months gives R² = 0.77, with a price elasticity of 0.79 (p < 0.001). A 10% price increase predicted 7.8% more signups. This model captures the full discovery pipeline — price movement → Google search → signup — in a single testable equation.
Applying that equation to Hive's actual price history produces a counterfactual: the number of signups Hive would have received each month if the Steem-era discovery pipeline still operated. The prediction comes with uncertainty bands — the model has a residual standard error of 0.47 in log space, meaning individual months can scatter ~60% around the prediction. We computed 80% and 95% prediction intervals to account for this.
The result:
In 43% of post-fork months, Hive's actual signups fell below even the 95% lower bound of the prediction. Under the Steem model, this should happen less than 5% of the time. The discovery pipeline isn't underperforming — it is absent.
The gap is not a one-time event. It is structural and it compounds. Over the post-Splinterlands period (February 2022 onward), cumulative retained users tell the story:
| Metric | Actual | Model estimate | 80% lower bound |
|---|---|---|---|
| Cumulative retained users | ~6,600 | ~23,700 | ~12,700 |
Even the pessimistic end of the 80% interval — the scenario where the Steem model systematically overpredicts — still produces nearly twice as many retained users as Hive actually got. The point estimate is 3.6×.
And these numbers are conservative. They apply the Steem-era price-to-signup relationship to Hive's actual prices, ignoring the flywheel feedback: more retained users would have raised the price, which would have attracted even more signups. The true counterfactual is higher, but we don't need to model the feedback loop. The direct pipeline gap alone is damning.
Caveats on the model. The Steem era had structural advantages beyond the name: steemit.com offered free instant account creation that no Hive frontend matches, and the 2017–18 bull run was crypto's first mainstream moment — the pool of casual searchers may have been larger. These factors mean the counterfactual likely overstates the gap somewhat. But even halving the estimate leaves Hive far below where a functional discovery pipeline would place it. The regression residuals also show positive autocorrelation (Durbin-Watson = 0.94), suggesting the confidence intervals may be somewhat too narrow. The 80% lower bound — still nearly double actual — is the more defensible figure.
Notably, the retention rate has held steady — or even improved:
| Scenario | Monthly new users | × Retention rate | = Retained users |
|---|---|---|---|
| Steem Jan 2018 (price peak) | 159,740 | ~33% | ~52,700 |
| Hive Mar 2021 (price peak) | 9,685 | ~40% | ~3,900 |
| Hive 2025 (current) | ~4,000 | ~35% | ~1,400 |
The problem is not that people leave. The problem is that they never arrive.
And the decay is self-reinforcing:
- Fewer users arrive → smaller community
- Smaller community → fewer people available to reply to newcomers
- Fewer replies → worse first experience → worse retention on those who do arrive
- Worse retention → even fewer retained users → lower price
- Lower price → fewer users arrive → repeat
Reply rates on newcomers' first posts have collapsed from 70–88% (Steem 2017–18) to 25–59% (Hive 2024). Median time to first reply has grown from 8 minutes to over 2 hours. This isn't because the community stopped caring — it's because there are fewer people available to reply. The community is shrinking, and as it shrinks it loses the capacity to welcome newcomers, which accelerates the shrinking.
Why the Spiral Can't Self-Correct
In a normal crypto cycle, a price pump should restart the flywheel. BTC goes up, altcoins follow, new users flood in, the virtuous cycle re-engages. This is how Steem recovered from its 2016 price crash — the 2017 bull market brought searchers who found the platform organically.
Hive cannot replicate this. Even if the price pumps 10×:
- Google searches for "Hive" won't respond (r = −0.14 during the 2021 bull run)
- People who do search "Hive" won't find the blockchain (zero first-page results)
- Month-over-month price changes have zero predictive power on signups (r = 0.10, p = 0.74)
The death spiral is self-reinforcing and externally unbreakable. The normal mechanism by which a crypto project recovers from decline — pump → discovery → growth → sustained value — cannot engage. Price can rise from market-wide forces, but the new users who would convert that rise into organic demand never arrive. The pump fades, price returns to baseline, and the spiral continues.
Can This Be Fixed Without a Rebrand?
SEO. The numbers make the case. When someone Googles "Hive," here is what they find on the first page:
- hive.com — project management software (funded SaaS)
- Apache Hive — data warehouse (Apache Foundation)
- thehive.ai — AI content moderation
- HIVE Digital Technologies — Bitcoin mining company (stock: HIVE)
- Hive (2026 film) — a Tubi horror movie
- Merriam-Webster — dictionary definition
- HIVE Festival — German techno festival
- HiveModern.com — furniture retailer
The blockchain does not appear. At all. Hive.blog currently ranks approximately 52nd — page six — for its own name. The SEO keyword difficulty for "Hive" is 65 (highly competitive). Meanwhile, Solana and Polkadot own their entire first page: 10 out of 10 results for "Solana" are about the blockchain; 9 out of 10 for "Polkadot."
Searching "Hive crypto" does fix the problem — hive.io ranks #1 with the qualifier. But that requires searchers to already know they are looking for a cryptocurrency. The whole point of a discovery pipeline is to capture the person who doesn't know yet.
Hive.blog has a Domain Authority of ~44 and roughly 9 million backlinks from 19,000 referring domains — a respectable profile for a community site. But it is competing for a keyword owned by commercial entities with marketing budgets, enterprise customers, and professional SEO teams. A decentralised community cannot win an SEO war for a generic English word. This is not a solvable resource allocation problem; it is a structural impossibility.
A fair objection here is that hive.io has genuine technical SEO failures — stale sitemaps, poor Core Web Vitals, identical meta tags across every page, no schema markup. Fix those, the argument goes, and the rankings improve. These failures are real, and fixing them is worth doing regardless of what the community decides about the name. But they don't address the structural problem the data identifies. When HIVE price pumps, people almost certainly do search "Hive" — but those searches are a small signal drowned in the noise of everyone searching for project management software, Apache Hive, smart home products, and a NASDAQ-listed mining company. The correlation collapsing to r = −0.22 reflects that: the blockchain-motivated searches can't move the needle on a term that 91% of searchers are using for something else. Better technical SEO on hive.io would improve its ranking position within that contest. It would not change the contest itself. And it is a contest Hive cannot win through volunteer effort, because the entities on page one are well-resourced organisations with full-time SEO teams continuously optimising their presence. A decentralised community cannot sustain that arms race. This is not a gap that can be closed by updating a sitemap — it is a permanent structural disadvantage baked into the name.
AEO (AI Engine Optimisation). The problem is about to get worse. A growing share of how people discover products is shifting from Google to AI assistants — ChatGPT, Gemini, Perplexity, Claude, and Google's own AI Overviews.
The numbers are stark: AI assistants now handle roughly 56% of the query volume of traditional search engines (45 billion monthly sessions). Google's share of all search activity dropped from 89% in 2023 to 71% in Q4 2025. 43% of users now query an AI assistant daily. Gartner projects that 50% of all searches will involve an AI assistant by 2028.
AI assistants do somewhat better than Google here. Ask ChatGPT or Perplexity "What is Hive?" and the blockchain typically appears — third or fourth, after the project management tool, Apache Hive, and often the smart home company. That's better than page 6 of Google. But "mentioned third or fourth in a list of things called Hive" is not discovery — it is disambiguation. The user has to already suspect a blockchain exists to pick it out. And when someone asks the more natural question — "what is a good decentralised social media platform?" — the blockchain's weak brand signal means it competes poorly against platforms with distinctive names.
The deeper problem is structural. AI systems synthesise from their training data, and the Hive blockchain is a small signal in a sea of other entities called "Hive." The same well-resourced companies that dominate the Google SERP — the SaaS platform, the Apache Foundation, the mining company — are producing exactly the kind of content, documentation, and press coverage that feeds AI training data. A decentralised community cannot match that output continuously. As AI-mediated discovery grows — Gartner projects 50% of searches will involve an AI assistant by 2028 — the competitive disadvantage compounds, and there is no way to buy your way out of it.
Google Ads. Buying "Hive crypto" keywords provides visibility to the small population who search with a qualifier. But roughly half of blockchain-motivated "Hive" searchers never add a qualifier — they see the wrong results and leave. And even for those who do search with a qualifier, the same well-resourced competitors can outbid a decentralised community indefinitely. Ads treat a symptom, not the disease, and only work while you're paying.
App-level brands. PeakD, 3Speak, and Splinterlands have unique, searchable names. But app-level discovery doesn't feed the token flywheel. When HIVE price rises, nobody discovers PeakD — they don't know it uses HIVE. And when an app succeeds on its own brand (as Splinterlands proved at massive scale), the ecosystem gets minimal spillover.
Crypto aggregators. CoinMarketCap and CoinGecko list HIVE. This helps crypto-native users who know to look there, but it misses the casual searcher — exactly the audience that drove Steem's growth.
None of these alternatives repair the specific mechanism that the data shows working on Steem and broken on Hive. And with the shift from traditional search to AI-mediated discovery, the window for fixing the name is narrowing — every year that passes builds the competing "Hive" entities' footprint in AI training data, making it harder for a future rebrand to reclaim the word even if we wanted to.
Other Blockchains Have Rebranded Successfully
Rebranding in crypto is not unprecedented. We analysed eight major rebrands:
| Project | Old Name | Year | Outcome (1-year, BTC-adjusted) |
|---|---|---|---|
| NEO | Antshares | 2017 | +226% |
| BEAM | Merit Circle | 2023 | +207% |
| Mantle | BitDAO | 2023 | +100% |
| Polygon | Matic | 2021 | +2,005% |
| Dash | Darkcoin | 2015 | −43% |
| Nano | RaiBlocks | 2018 | −29% |
| Sky | MakerDAO | 2024 | −67% |
| Sonic | Fantom | 2024 | −79% |
The results are mixed — rebranding is not a guaranteed price pump, and some rebrands coincided with bear markets that overwhelmed any brand benefit. But the successful cases share a pattern: they moved from a less distinctive or less marketable name to one with clearer identity. Antshares → NEO. Merit Circle → BEAM. Matic → Polygon. The projects that gained from rebranding did so because the new name better served their growth strategy.
Hive's situation is more extreme than any of these. None of these projects had a name that was actively blocking their discovery pipeline. They rebranded for positioning; Hive would be rebranding for survival.
The Diagnosis
The evidence points to a single binding constraint:
- The flywheel is real — retained users predict price (p < 0.001), price predicts acquisition (p = 0.017)
- The flywheel requires search discovery — on Steem, price and search correlated at r = 0.86
- Search discovery is broken by the name — on Hive, the correlation is r = −0.22 (not significant)
- Tokens with unique names don't have this problem — Solana r = 0.72, Polkadot r = 0.58
- The traffic confirms it — Steemit still draws 3× more visitors than all Hive frontends combined, powered by organic search
- Hive is invisible on its own SERP — page 6, position 52, behind project management software, a horror movie, and a furniture store
- AI discovery doesn't fix it — AI assistants mention Hive third or fourth in disambiguation lists, not as a primary recommendation
- No alternative fixes it — SEO can't compete, app brands don't feed the token, ads don't scale, AEO can't be bought
- Every downstream improvement is wasted — retention, curation, apps all work at the individual level but cannot compound on a shrinking base
- The spiral can't self-correct — even a 10× price pump produces no organic discovery
The name is the bottleneck. Everything downstream of the name — discovery, onboarding, retention, community — is either healthy or fixable. But none of it matters if nobody can find us.
What We're Asking
We have submitted a DHF proposal. This is a return proposal — it sends 1 HBD per day back to the DHF. The amount is symbolic; the vote is the point. It asks a single question: do you agree the name is the problem?
It does not propose a name. It does not commit to a timeline or a budget. If the community votes it above the return proposal threshold, the next step is concrete proposals: specific names, implementation plans, cost estimates, DHF funding requests. Those proposals will stand or fall on their own merits.
If it cannot clear the return proposal — then the idea is settled and we move on.
But the data is clear. Hive is in a death spiral with an identifiable cause and a fixable solution. The question is whether we have the will to fix it.
Caveats
Google Trends data is normalised and approximate. Values are relative (0–100 within each query), not absolute search counts. Cross-query comparisons require the same tokens in both queries. We used same-query comparisons and the uplift method for cross-query estimates.
Correlation is not causation. The Steem-era pipeline (price → search → signup) is inferred from timing and correlation, not proven causally. Both search and signups could be driven by a common third factor. The structural argument (unique word = findable, generic word = unfindable) holds regardless of exact mechanism.
The cross-token comparison has limitations. Solana and Polkadot are orders of magnitude larger than Hive. They may have captured their search terms through sheer market cap. The comparison demonstrates the gradient from unique to generic names, not a controlled experiment.
Rebrand case studies are confounded. NEO and Polygon rebranded during bull markets; Nano and Sonic during bears. Price performance after a rebrand reflects market conditions as much as the name change. We included BTC-adjusted returns to partially control for this, but the sample is small.
Discoverability is necessary but not sufficient. Even with a searchable name, Hive needs a frictionless onboarding path. No current frontend offers free account creation comparable to what steemit.com provided. Fixing the name opens the top of the funnel; converting arrivals into users is a separate challenge.
The counterfactual model overstates the name's contribution. The Steem-era regression captures the full price-to-signup pipeline, but the Steem era also had free instant account creation via steemit.com and a less crowded crypto landscape. Some portion of the gap between the model's predictions and Hive's actual signups is attributable to these factors rather than the name alone. The 80% lower bound (~2× actual retained users) is the safer reference point; the point estimate (~3.6×) assumes the name explains the entire gap.
A rebrand carries real costs. Exchange relisting, frontend updates, wallet changes, developer tooling, community coordination, and potential confusion during transition. This analysis demonstrates the problem; it does not claim to enumerate the full cost of the solution. That work belongs in the follow-up proposals.
Data: Google Trends (queried May 2026 via pytrends), CryptoCompare (daily prices for HIVE, SOL, DOT, AVAX, ATOM, STEEM), HiveSQL (account and cohort data), CoinGecko (top-50 audit), SimilarWeb and Semrush (web traffic and SEO metrics, 2025–2026),
If you support a rebrand of Hive, please find the proposal here and vote:
https://peakd.com/me/proposals
Please also reblog this post, and the proposal post, to draw attention to this issue.