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What is Tokenomics? - Complete Guide for Investors

Explore the concept of tokenomics and its significance in the crypto world. Get insights into how token economics impacts investments.
S. Vishwa
8 Minutes
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Tokenomics is a buzzword in the crypto industry that has many investors, both seasoned and newcomers, scratching their heads. 

In this comprehensive guide, we'll unravel the complexities of tokenomics and simplify it into an actionable framework. By the time you're done reading, you'll not only understand what tokenomics is but also how to evaluate it for your investment decisions.

What is Tokenomics?

Tokenomics combines the words "token" and "economics." It refers to the financial structure of a cryptocurrency. Like the rules of a game, tokenomics defines how tokens work within a particular blockchain ecosystem. If you're an investor, understanding tokenomics is key to making informed decisions.

Simple Definition - Tokenomics involves understanding the purpose, functionality, and the strategic design of a token within a blockchain ecosystem. 

It's like understanding the DNA of a cryptocurrency. By knowing the details of how a token works, you can decide if it's a wise investment.

Importance of Tokenomics - Tokenomics impacts everything from the total supply of tokens to how they're distributed, secured, and used. Poor tokenomics can lead to loss of investment, while thoughtful design might provide a roadmap to success.

Key Components of Tokenomics

1. Total Supply - The total supply refers to the maximum number of tokens that will ever exist for a particular cryptocurrency. This aspect is crucial because it introduces the concept of scarcity. 

Just like precious metals, if a token has a limited supply, it might increase its demand, and subsequently, its value. Some cryptocurrencies, like Bitcoin, have a capped supply (21 million), while others might have an unlimited supply. Knowing the total supply can give insights into how rare or abundant a token might be in the market.

2. Distribution - Distribution outlines how tokens are allocated among different stakeholders, such as developers, investors, the community, and even reserve funds. 

This distribution model needs to be transparent and fair to maintain trust within the ecosystem. An uneven distribution might lead to a concentration of power or wealth, potentially making the token more susceptible to manipulation. 

For instance, initial coin offerings (ICOs) often detail how the tokens will be distributed, and this information can be vital in assessing the token's long-term viability.

3. Utility - Utility describes the functionality and purpose of a token within its ecosystem. Is the token simply a store of value, or does it have a specific use within a decentralized application? Understanding a token's utility can provide insights into its intrinsic value. 

For example, some tokens might grant voting rights in the project's development decisions, while others might be used to pay for services within the network. A token with clear and compelling utility is often seen as a positive indicator for investors.

4. Security - The security component of tokenomics involves understanding the measures in place to protect the token and the overall network. This can include the consensus mechanism used (Proof of Work or Proof of Stake), how the network guards against attacks, and the security of wallets and exchanges where the tokens are held. 

Security is paramount, as vulnerabilities can lead to loss of funds or trust in the network. Assessing the security measures in place and ensuring they meet high standards can save an investor from potential pitfalls.

These key components of tokenomics are integral to understanding how a token operates within its blockchain ecosystem. Analyzing these factors allows an investor to make well-informed decisions, aligning investments with risk tolerance, and potential rewards. 

How to Analyze Tokenomics?

Understanding these intricate details is vital for any investor who aims to make informed decisions. Here's how you can analyze tokenomics:

Research Whitepapers - Most crypto projects outline their tokenomics in a document known as a whitepaper. This is often the primary source for understanding a token's supply, distribution, utility, and security. 

By thoroughly reading and comprehending a project's whitepaper, investors can discern the intentions behind the token and its potential value. This isn't just a cursory glance; it requires a careful examination to understand the philosophy, technology, and mechanics behind the token.

Check Community Engagement - Community engagement is a vital sign of a project's health. A vibrant and engaged community often signifies strong support and belief in the project's mission. 

Investors can explore forums, social media channels, and even physical meet-ups to gauge the pulse of the community. By interacting with community members or simply observing the discussions, one can get insights into how the project is perceived, potential concerns, and the overall sentiment.

Evaluate Utility and Demand - Understanding a token's utility means discerning its purpose and functionality within the ecosystem. Is it merely a speculative asset, or does it serve a unique function? Evaluating the real-world application and demand for the token can provide clues to its intrinsic value. 

For instance, if a token is required to access a service within a popular decentralized application, it likely has tangible utility. Coupling this with an assessment of the demand for that service can provide a solid foundation for investment decisions.

Consider the Economic Models - Different projects may employ various economic models, which could include elements like inflation, deflation, or even a hybrid approach. 

Understanding these models helps in predicting how the token's value might behave over time. For instance, a token with a deflationary model may increase in value as the supply decreases, while inflationary models might have the opposite effect.

Examine the Regulatory Compliance - Compliance with local and international regulations is a factor that should not be overlooked. Ensuring that the project adheres to legal requirements can minimize potential legal risks and contribute to its legitimacy.

Examples of Tokenomics

Bitcoin (BTC)

Total Supply: 21 million

Distribution: Mining

Utility: Currency

Security: Proof of Work (PoW)

Ethereum (ETH)

Total Supply: No hard cap

Distribution: Mining, also pre-mined

Utility: Smart Contracts

Security: Transitioning from PoW to Proof of Stake (PoS)

Mistakes to Avoid

Investing without understanding tokenomics can lead to losses. Avoid these common mistakes:

Ignoring the Whitepaper: Always read and understand the project's whitepaper.

Following the Crowd: Don't just follow trends; make decisions based on sound analysis.

Overlooking Security: Ensure the project has robust security measures.

Actionable Steps for Investors

Study the Whitepaper: Get details of the tokenomics from the project's official documents.

Engage with the Community: Participate in forums and social media to understand the community's view.

Evaluate Real-World Utility: Ensure the token has a clear purpose and demand.

Consult with a Financial Expert if Needed: Crypto investments are risky, and professional advice can be invaluable.

Frequently Asked Questions

Q1. Can tokenomics change after a project’s launch? How does this impact investors?

Yes, tokenomics can change through updates to the project's protocol or governance decisions. Such changes may impact token value, utility, or distribution. Investors should stay informed by following the project's official channels to understand any changes and assess their potential impact.

Q2. How can I verify the authenticity of the information on a project's tokenomics?

Always refer to official sources like the project's whitepaper, website, and credible crypto analysis platforms. Beware of misinformation from unofficial channels. Participating in community forums and reaching out to the team directly can also help verify information.

Q3. How do forks in a blockchain project affect tokenomics?

Forks can create new tokens with different tokenomics. This might affect supply, demand, utility, and overall value. Understanding the reasons for the fork and the new tokenomics can guide investment decisions post-fork.

Q4. How do token burning and minting fit into tokenomics?

Token burning (destroying tokens) and minting (creating new tokens) can be part of a project's economic model. Burning can increase scarcity, potentially raising value, while minting may increase supply, possibly lowering value. Both mechanisms are used to maintain control over a token's supply and demand dynamics.

Q5. What's the difference between a token's circulating supply and total supply in tokenomics?

Total supply refers to all tokens created, while circulating supply refers to tokens currently available in the market. Understanding the difference helps investors gauge scarcity and potential market saturation, influencing investment strategies.

Q6. How does staking fit into the tokenomics of a project?

Staking involves locking up tokens to support network operations like validation. It can be a vital part of the economic model, affecting supply and demand, providing incentives to holders, and enhancing network security.

Q7. How do governance tokens and tokenomics interact?

Governance tokens allow holders to participate in decision-making within a project. Their inclusion in tokenomics reflects a commitment to decentralization and community involvement, and they can be essential in shaping the project's direction, including changes to tokenomics itself.

Q8. Can tokenomics help in identifying scams or fraudulent projects?

Analyzing tokenomics can uncover red flags like unfair distribution, lack of clear utility, or non-transparent practices. Investors should use tokenomics as part of a broader due diligence process to assess legitimacy and avoid potential scams.

Conclusion

Tokenomics is a complex but essential part of evaluating crypto investments. By understanding the total supply, distribution, utility, and security, you'll be empowered to make informed decisions.

Investing without understanding tokenomics can lead to losses. Remember, do your research, stay updated with current market trends and invest wisely by applying these principles.

Disclaimer

The information provided on this website does not constitute investment advice, financial advice, trading advice, or any other sort of advice and you should not treat any of the website's content as such.

Token Metrics does not recommend that any cryptocurrency should be bought, sold, or held by you. Do conduct your own due diligence and consult your financial advisor before making any investment decisions.

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About Token Metrics
Token Metrics: AI-powered crypto research and ratings platform. We help investors make smarter decisions with unbiased Token Metrics Ratings, on-chain analytics, and editor-curated “Top 10” guides. Our platform distills thousands of data points into clear scores, trends, and alerts you can act on.
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Recent Posts

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Support and Resistance API: Auto-Calculate Smart Levels for Better Trades

Token Metrics Team
4

Most traders still draw lines by hand in TradingView. The support and resistance API from Token Metrics auto-calculates clean support and resistance levels from one request, so your dashboard, bot, or alerts can react instantly. In minutes, you’ll call /v2/resistance-support, render actionable levels for any token, and wire them into stops, targets, or notifications. Start by grabbing your key on Get API Key, then Run Hello-TM and Clone a Template to ship a production-ready feature fast.

What You’ll Build in 2 Minutes

A minimal script that fetches Support/Resistance via /v2/resistance-support for a symbol (e.g., BTC, SOL).

  • A one-liner curl to smoke-test your key.
  • A UI pattern to display nearest support, nearest resistance, level strength, and last updated time.

Next Endpoints to add

  • /v2/trading-signals (entries/exits)
  • /v2/hourly-trading-signals (intraday updates)
  • /v2/tm-grade (single-score context)
  • /v2/quantmetrics (risk/return framing)

Why This Matters

Precision beats guesswork. Hand-drawn lines are subjective and slow. The support and resistance API standardizes levels across assets and timeframes, enabling deterministic stops and take-profits your users (and bots) can trust.

Production-ready by design. A simple REST shape, predictable latency, and clear semantics let you add levels to token pages, automate SL/TP alerts, and build rule-based execution with minimal glue code.

Where to Find

Need the Support and Resistance data? The cURL request for it is in the top right of the API Reference for quick access.

👉 Keep momentum: Get API Key • Run Hello-TM • Clone a Template

How It Works (Under the Hood)

The Support/Resistance endpoint analyzes recent price structure to produce discrete levels above and below current price, along with strength indicators you can use for priority and styling. Query /v2/resistance-support?symbol=<ASSET>&timeframe=<HORIZON> to receive arrays of level objects and timestamps.

Polling vs webhooks. For dashboards, short-TTL caching and batched fetches keep pages snappy. For bots and alerts, use queued jobs or webhooks (where applicable) to avoid noisy, bursty polling—especially around market opens and major events.

Production Checklist

  • Rate limits: Respect plan caps; add client-side throttling.
  • Retries/backoff: Exponential backoff with jitter for 429/5xx; log failures.
  • Idempotency: Make alerting and order logic idempotent to prevent duplicates.
  • Caching: Memory/Redis/KV with short TTLs; pre-warm top symbols.
  • Batching: Fetch multiple assets per cycle; parallelize within rate limits.
  • Threshold logic: Add %-of-price buffers (e.g., alert at 0.3–0.5% from level).
  • Error catalog: Map common 4xx/5xx to actionable user guidance; keep request IDs.
  • Observability: Track p95/p99; measure alert precision (touch vs approach).
  • Security: Store API keys in a secrets manager; rotate regularly.

Use Cases & Patterns

  • Bot Builder (Headless): Use nearest support for stop placement and nearest resistance for profit targets. Combine with /v2/trading-signals for entries/exits and size via Quantmetrics (volatility, drawdown).
  • Dashboard Builder (Product): Add a Levels widget to token pages; badge strength (e.g., High/Med/Low) and show last touch time. Color the price region (below support, between levels, above resistance) for instant context.
  • Screener Maker (Lightweight Tools): “Close to level” sort: highlight tokens within X% of a strong level. Toggle alerts for approach vs breakout events.
  • Risk Management: Create policy rules like “no new long if price is within 0.2% of strong resistance.” Export daily level snapshots for audit/compliance.

Next Steps

  • Get API Key — generate a key and start free.
  • Run Hello-TM — verify your first successful call.
  • Clone a Template — deploy a levels panel or alerts bot today.
  • Watch the demo: Compare plans: Scale confidently with API plans.

FAQs

1) What does the Support & Resistance API return?

A JSON payload with arrays of support and resistance levels for a symbol (and optional timeframe), each with a price and strength indicator, plus an update timestamp.

2) How timely are the levels? What are the latency/SLOs?

The endpoint targets predictable latency suitable for dashboards and alerts. Use short-TTL caching for UIs, and queued jobs or webhooks for alerting to smooth traffic.

3) How do I trigger alerts or trades from levels?

Common patterns: alert when price is within X% of a level, touches a level, or breaks beyond with confirmation. Always make downstream actions idempotent and respect rate limits.

4) Can I combine levels with other endpoints?

Yes—pair with /v2/trading-signals for timing, /v2/tm-grade for quality context, and /v2/quantmetrics for risk sizing. This yields a complete decide-plan-execute loop.

5) Which timeframe should I use?

Intraday bots prefer shorter horizons; swing/position dashboards use daily or higher-timeframe levels. Offer a timeframe toggle and cache results per setting.

6) Do you provide SDKs or examples?

Use the REST snippets above (JS/Python). The docs include quickstarts, Postman collections, and templates—start with Run Hello-TM.

7) Pricing, limits, and enterprise SLAs?

Begin free and scale as you grow. See API plans for rate limits and enterprise SLA options.

Disclaimer

This content is for educational purposes only and does not constitute financial advice. Always conduct your own research before making any trading decisions.

Research

Quantmetrics API: Measure Risk & Reward in One Call

Token Metrics Team
5

Most traders see price—quants see probabilities. The Quantmetrics API turns raw performance into risk-adjusted stats like Sharpe, Sortino, volatility, drawdown, and CAGR so you can compare tokens objectively and build smarter bots and dashboards. In minutes, you’ll query /v2/quantmetrics, render a clear performance snapshot, and ship a feature that customers trust. Start by grabbing your key at Get API Key, Run Hello-TM to verify your first call, then Clone a Template to go live fast.

What You’ll Build in 2 Minutes

  • A minimal script that fetches Quantmetrics for a token via /v2/quantmetrics (e.g., BTC, ETH, SOL).
  • A smoke-test curl you can paste into your terminal.
  • A UI pattern that displays Sharpe, Sortino, volatility, max drawdown, CAGR, and lookback window.

Next Endpoints to Add

  • /v2/tm-grade (one-score signal)
  • /v2/trading-signals
  • /v2/hourly-trading-signals (timing)
  • /v2/resistance-support (risk placement)
  • /v2/price-prediction (scenario planning)

Why This Matters

Risk-adjusted truth beats hype. Price alone hides tail risk and whipsaws. Quantmetrics compresses edge, risk, and consistency into metrics that travel across assets and timeframes—so you can rank universes, size positions, and communicate performance like a professional.

Built for dev speed

A clean REST schema, predictable latency, and easy auth mean you can plug Sharpe/Sortino into bots, dashboards, and screeners without maintaining your own analytics pipeline. Pair with caching and batching to serve fast pages at scale.

Where to Find

The Quant Metrics cURL request is located in the top right of the API Reference, allowing you to easily integrate it with your application.

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How It Works (Under the Hood)

Quantmetrics computes risk-adjusted performance over a chosen lookback (e.g., 30d, 90d, 1y). You’ll receive a JSON snapshot with core statistics:

  • Sharpe ratio: excess return per unit of total volatility.
  • Sortino ratio: penalizes downside volatility more than upside.
  • Volatility: standard deviation of returns over the window.
  • Max drawdown: worst peak-to-trough decline.
  • CAGR / performance snapshot: geometric growth rate and best/worst periods.

Call /v2/quantmetrics?symbol=<ASSET>&window=<LOOKBACK> to fetch the current snapshot. For dashboards spanning many tokens, batch symbols and apply short-TTL caching. If you generate alerts (e.g., “Sharpe crossed 1.5”), run a scheduled job and queue notifications to avoid bursty polling.

Production Checklist

  • Rate limits: Understand your tier caps; add client-side throttling and queues.
  • Retries & backoff: Exponential backoff with jitter; treat 429/5xx as transient.
  • Idempotency: Prevent duplicate downstream actions on retried jobs.
  • Caching: Memory/Redis/KV with short TTLs; pre-warm popular symbols and windows.
  • Batching: Fetch multiple symbols per cycle; parallelize carefully within limits.
  • Error catalog: Map 4xx/5xx to clear remediation; log request IDs for tracing.
  • Observability: Track p95/p99 latency and error rates; alert on drift.
  • Security: Store API keys in secrets managers; rotate regularly.

Use Cases & Patterns

  • Bot Builder (Headless): Gate entries by Sharpe ≥ threshold and drawdown ≤ limit, then trigger with /v2/trading-signals; size by inverse volatility.
  • Dashboard Builder (Product): Add a Quantmetrics panel to token pages; allow switching lookbacks (30d/90d/1y) and export CSV.
  • Screener Maker (Lightweight Tools): Top-N by Sortino with filters for volatility and sector; add alert toggles when thresholds cross.
  • Allocator/PM Tools: Blend CAGR, Sharpe, drawdown into a composite score to rank reallocations; show methodology for trust.
  • Research/Reporting: Weekly digest of tokens with Sharpe ↑, drawdown ↓, and volatility ↓.

Next Steps

  • Get API Key — start free and generate a key in seconds.
  • Run Hello-TM — verify your first successful call.
  • Clone a Template — deploy a screener or dashboard today.
  • Watch the demo: VIDEO_URL_HERE
  • Compare plans: Scale with API plans.

FAQs

1) What does the Quantmetrics API return?

A JSON snapshot of risk-adjusted metrics (e.g., Sharpe, Sortino, volatility, max drawdown, CAGR) for a symbol and lookback window—ideal for ranking, sizing, and dashboards.

2) How fresh are the stats? What about latency/SLOs?

Responses are engineered for predictable latency. For heavy UI usage, add short-TTL caching and batch requests; for alerts, use scheduled jobs or webhooks where available.

3) Can I use Quantmetrics to size positions in a live bot?

Yes—many quants size inversely to volatility or require Sharpe ≥ X to trade. Always backtest and paper-trade before going live; past results are illustrative, not guarantees.

4) Which lookback window should I choose?

Short windows (30–90d) adapt faster but are noisier; longer windows (6–12m) are steadier but slower to react. Offer users a toggle and cache each window.

5) Do you provide SDKs or examples?

REST is straightforward (JS/Python above). Docs include quickstarts, Postman collections, and templates—start with Run Hello-TM.

6) Polling vs webhooks for quant alerts?

Dashboards usually use cached polling. For threshold alerts (e.g., Sharpe crosses 1.0), run scheduled jobs and queue notifications to keep usage smooth and idempotent.

7) Pricing, limits, and enterprise SLAs?

Begin free and scale up. See API plans for rate limits and enterprise SLA options.

Disclaimer

All information provided in this blog is for educational purposes only. It is not intended as financial advice. Users should perform their own research and consult with licensed professionals before making any investment or trading decisions.

Research

Crypto Trading Signals API: Put Bullish/Bearish Calls Right in Your App

Token Metrics Team
4

Timing makes or breaks every trade. The crypto trading signals API from Token Metrics lets you surface bullish and bearish calls directly in your product—no spreadsheet wrangling, no chart gymnastics. In this guide, you’ll hit the /v2/trading-signals endpoint, display actionable signals on a token (e.g., SOL, BTC, ETH), and ship a conversion-ready feature for bots, dashboards, or Discord. Start by creating a key on Get API Key, then Run Hello-TM and Clone a Template to go live fast.

What You’ll Build in 2 Minutes

  • A minimal script that fetches Trading Signals via /v2/trading-signals for one symbol (e.g., SOL).
  • A copy-paste curl to smoke-test your key.
  • A UI pattern to render signal, confidence/score, and timestamp in your dashboard or bot.

Endpoints to add next

  • /v2/hourly-trading-signals (intraday updates)
  • /v2/resistance-support (risk placement)
  • /v2/tm-grade (one-score view)
  • /v2/quantmetrics (risk/return context)

Why This Matters

Action over analysis paralysis. Traders don’t need more lines on a chart—they need an opinionated call they can automate. The trading signals API compresses technical momentum and regime reads into Bullish/Bearish events you can rank, alert on, and route into strategies.

Built for dev speed and reliability. A clean schema, predictable performance, and straightforward auth make it easy to wire signals into bots, dashboards, and community tools. Pair with short-TTL caching or webhooks to minimize polling and keep latency low.

Where to Find

You can find the cURL request for Crypto Trading Signals in the top right corner of the API Reference. Use it to access the latest signals!

Live Demo & Templates

  • Trading Bot Starter: Use Bullish/Bearish calls to trigger paper trades; add take-profit/stop rules with Support/Resistance.
  • Dashboard Signal Panel: Show the latest call, confidence, and last-updated time; add a history table for context.
  • Discord/Telegram Alerts: Post signal changes to a channel with a link back to your app.

How It Works (Under the Hood)

Trading Signals distill model evidence (e.g., momentum regimes and pattern detections) into Bullish or Bearish calls with metadata such as confidence/score and timestamp. You request /v2/trading-signals?symbol=<ASSET> and render the most recent event, or a small history, in your UI.

For intraday workflows, use /v2/hourly-trading-signals to update positions or alerts more frequently. Dashboards typically use short-TTL caching or batched fetches; headless bots lean on webhooks, queues, or short polling with backoff to avoid spiky API usage.

Production Checklist

  • Rate limits: Know your tier caps; add client-side throttling and queues.
  • Retries/backoff: Exponential backoff with jitter; treat 429/5xx as transient.
  • Idempotency: Guard downstream actions (don’t double-trade on retries).
  • Caching: Memory/Redis/KV with short TTLs for reads; pre-warm popular symbols.
  • Webhooks & jobs: Prefer webhooks or scheduled workers for signal change alerts.
  • Pagination/Bulk: Batch symbols; parallelize with care; respect limits.
  • Error catalog: Map common 4xx/5xx to clear fixes; log request IDs.
  • Observability: Track p95/p99 latency, error rate, and alert delivery success.
  • Security: Keep keys in a secrets manager; rotate regularly.

Use Cases & Patterns

  • Bot Builder (Headless): Route Bullish into candidate entries; confirm with /v2/resistance-support for risk and TM Grade for quality.
  • Dashboard Builder (Product): Add a “Signals” module per token; color-code state and show history for credibility.
  • Screener Maker (Lightweight Tools): Filter lists by Bullish state; sort by confidence/score; add alert toggles.
  • Community/Discord: Post signal changes with links to token pages; throttle to avoid noise.
  • Allocator/PM Tools: Track signal hit rates by sector/timeframe to inform position sizing (paper-trade first).

Next Steps

  1. Get API Key — create a key and start free.
  2. Run Hello-TM — confirm your first successful call.
  3. Clone a Template — deploy a bot, dashboard, or alerting tool today.

FAQs

1) What does the Trading Signals API return?

A JSON payload with the latest Bullish/Bearish call for a symbol, typically including a confidence/score and generated_at timestamp. You can render the latest call or a recent history for context.

2) Is it real-time? What about latency/SLOs?

Signals are designed for timely, programmatic use with predictable latency. For faster cycles, use /v2/hourly-trading-signals. Add caching and queues/webhooks to reduce round-trips.

3) Can I use the signals in a live trading bot?

Yes—many developers do. A common pattern is: Signals → candidate entry, Support/Resistance → stop/targets, Quantmetrics → risk sizing. Always backtest and paper-trade before going live.

4) How accurate are the signals?

Backtests are illustrative, not guarantees. Treat signals as one input in a broader framework with risk controls. Evaluate hit rates and drawdowns on your universe/timeframe.

5) Do you provide SDKs and examples?

You can integrate via REST using JavaScript and Python snippets above. The docs include quickstarts, Postman collections, and templates—start with Run Hello-TM.

6) Polling vs webhooks for alerts?

Dashboards often use cached polling. For bots/alerts, prefer webhooks or scheduled jobs and keep retries idempotent to avoid duplicate trades or messages.

7) Pricing, limits, and enterprise SLAs?

Begin free and scale as you grow. See API plans for allowances; enterprise SLAs and support are available.

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