This article will deeply analyze Arweave's permanent storage architecture, AO's hyper-parallel computing model, and how the two will promote the future development of on-chain autonomous agents. At the same time, it will also explore the challenges faced by AR and AO, token market dynamics, and how to participate in them.This article will deeply analyze Arweave's permanent storage architecture, AO's hyper-parallel computing model, and how the two will promote the future development of on-chain autonomous agents. At the same time, it will also explore the challenges faced by AR and AO, token market dynamics, and how to participate in them.

AO is about to be released: Can Arweave regain momentum?

2025/01/15 19:30

AO is about to be released: Can Arweave regain momentum?

Original article: ArringtonCapital

Compiled by: Yuliya, PANews

On February 8, 2025, the AO mainnet will be launched, which is an important milestone for the AI and cryptocurrency communities. AO aims to provide a highly parallel computing layer for proxy applications, and Arweave, the permanent data storage network behind it, plays a key role in this. This article will deeply analyze Arweave's permanent storage architecture, AO's hyper-parallel computing model, and how the two will drive the future development of on-chain autonomous agents. At the same time, it will also explore the challenges faced by AR and AO, market dynamics, and how to participate in them.

Arweave Overview

Arweave is a decentralized permanent data storage network. Users only need to pay a one-time storage fee to obtain permanent data storage services. Unlike other storage networks (such as Filecoin) that require continuous payment, Arweave uses a unique block structure called "blockweave". Each new block is not only linked to the previous block, but also randomly linked to earlier historical blocks, ensuring that miners must hold complete historical data to generate new blocks, thereby achieving long-term data preservation.

Arweave's native token AR is used to pay for storage and reward miners. When users upload new data and pay fees, about 85% of the tokens are deposited into a fund for future miner rewards. This design ensures that miners' incentives are independent of user activity and enhances confidence in the permanent storage of data.

Growth trajectory

Since launching in June 2018, Arweave usage has grown significantly in 2021. The following chart tracks weekly data uploads since the network launch:

AO is about to be released: Can Arweave regain momentum?

Data storage peaked in September 2021, bottomed out in June 2023, and has been climbing steadily since then. The chart below breaks down the type of data uploaded each month.

AO is about to be released: Can Arweave regain momentum?

Arweave usage over time (by size)

In 2021, the rise of NFTs drove the first major increase in demand for Arweave data storage. Creators began uploading JPEGs and images to Arweave instead of relying on centralized hosting services, a trend that has led to a surge in Arweave usage. Due to its permanent and decentralized nature, Arweave has become an ideal choice for NFT artwork data storage.

Since 2023, a range of new use cases have emerged. Of all the categories, applications take up the most storage space. These are mainly packager applications that package multiple transactions and data together and publish them to Arweave. These include Bundlr (the team has changed its name to Irys.xyz and will launch its own data chain in addition to the packager application) and Ardrive Turbo . The data packaged by these applications includes content that may have previously been classified as pictures, videos, or other blockchain data. In addition to these packager applications, there are other projects that are taking advantage of Arweave's permanent storage capabilities, including Lens' social application Hey , content publishing platform Mirror , and AI application scenario Ritual .

Judging from the number of transactions, although Arweave charges fees based on the size of the stored data, the growing number of transactions may indicate the future development direction of Arweave.

AO is about to be released: Can Arweave regain momentum?

Arweave usage over time (by number of transactions)

Transaction volume data shows that the two fastest growing use cases in the blockchain ecosystem are Redstone and AO .

  1. Redstone

    Redstone is one of the fastest growing oracle networks in the crypto space, providing price data for multiple assets across all major EVM chains. The network’s rapid growth is due to its expanding partnerships and product features.

  2. AO

    AO is a parallel computing and proxy messaging layer built on Arweave. Although it is still in the testnet stage, its mainnet is scheduled to be launched in February 2025. AO is designed to provide efficient computing infrastructure for proxy applications and use Arweave's permanent storage capabilities to support on-chain autonomous agents.

Criticisms of Arweave

Although Arweave’s storage model has been recognized, it has also faced some criticism, especially its low fee income. The following is a comparison of the PE ratios of different blockchains:

AO is about to be released: Can Arweave regain momentum?

Looking at the price/fee ratio, Arweave outperforms only Avalanche among L1 chains. A lower ratio indicates that users pay higher fees relative to the fully diluted value (FDV) of the network. These figures reflect the total fees generated but do not take into account miner payments or Arweave's endowment fund contributions. Because Arweave allocates a larger portion of fees to miners, its short-term profits may appear smaller than other chains.

AR Token Performance

In 2024, AR saw significant growth after the announcement of the AO project. After the announcement, the price of Arweave tokens soared from less than $10 per token to more than $40. The market showed strong interest in the possibilities that AO brought to cryptocurrencies and the expected growth in Arweave activity.

AR holders can accumulate AO tokens by holding AR in their wallets starting in February 2024. Currently, 33% of newly created AO is in circulation to holders, and these tokens will be available for transfer when the AO mainnet is launched in February 2025.

After the mainnet launch, AR holders will continue to receive one-third of the AO token issuance until the creation cap of 2.1 million AO tokens is reached. These rewards are calculated every five minutes at a monthly rate of 1.425% of the remaining supply, meaning that token issuance will decrease over time.

AO is about to be released: Can Arweave regain momentum?

AR Price (USD)

As the overall market fell, AR's price also fell in the summer. Compared with tokens with AI value attributes such as RENDER, TAO, and NEAR, AR's performance is relatively lagging. On-chain capital flow may be an important factor in this phenomenon.

Since September 2024, the market has observed a large number of AR tokens being sold by a large investor. The identity of this investor has not been confirmed, although there are clues. The wallet address dRFuVE-s6-TgmykU4Zqn246AR2PIsf3HhBhZ0t5-WXE received more than 10 million AR tokens in November 2021 (the total supply of AR is less than 66 million). The wallet had transfer records before 2023 and still held 5 million tokens in 2024 (about $80 million at the current price of $16).

On September 6, the wallet moved the remaining 5 million tokens to two new addresses. These addresses subsequently transferred the tokens to exchanges, indicating that these may be market maker addresses. Of the 5 million tokens, approximately 1.35 million tokens remain in addresses presumably belonging to market makers, waiting to be transferred to exchanges.

The transfer of funds from two addresses to the same destination address of the exchange indicates that this is likely to be operated by the same market maker. This wave of selling pressure accounts for a large proportion of the circulating supply, exceeding 7% of the total AR tokens. Market analysis believes that once the remaining tokens are sold, the downward pressure on the AR market may be alleviated.

AO Overview

AO is a decentralized "hyper-parallelized" network that breaks through the traditional limitations of on-chain computing scale and type while maintaining the verifiability of all operations. The core of AO is a messaging layer that supports independent and parallel processes and uses Arweave to provide permanent data storage, ensuring that all updates and interactions are permanently recorded.

"AO" stands for "Actor Oriented". Developers can build modular programs (actors). Each actor can choose its own virtual machine (VM), consensus mechanism and payment model, and communicate with other actors through standardized message formats. This design allows cloud applications (such as Amazon EC2) to access AO's decentralized network and collaborate with decentralized smart contracts to achieve common goals.

AO Features

  1. Existing applications

    Some AO agents are already in use. For example, one agent can continuously optimize the returns of crypto assets across multiple lending protocols; another agent can automatically execute fixed investment strategies on DEX based on user-defined parameters. These agents use trusted execution environments (TEEs) to protect user privacy and allow users to host private keys, thereby achieving full autonomy without the need for additional instructions.

  2. Automatic wake-up function

    Unlike other Layer1s, AO programs can "wake up" autonomously without external calls. This design supports fully autonomous services. For example, the yield optimization agent can reallocate assets to higher-yield strategies while users are sleeping, without manual triggering.

AO Architecture

1. Processes:

A process is equivalent to a single "actor" on an AO, starting from an initial state and recording all received messages. Data is stored on Arweave, ensuring that it cannot be lost or censored. By separating data recording from actual computation, AO can handle larger tasks than a typical blockchain.

2. Messages:

Messages are the way processes and users interact, and are sent over the network with unique IDs for easy tracking. Message delivery must be correct to be delivered, which provides flexibility in flow control while ensuring that messages are permanently recorded.

3. Scheduler Units:

The scheduling unit adds incremental time slot numbers to messages and ensures that they are uploaded to Arweave, maintaining a consistent record of message sequences. It can be centralized or decentralized depending on the use case requirements.

4. Compute Units:

Computing units are responsible for the actual running of the process and can freely choose the process to be computed, forming a competitive market for computing services. After completing the work, they will return a signed proof of the process state change.

5. Messenger Units:

The message passing unit is responsible for message transmission in the network, ensuring that the message is recorded on Arweave by the scheduling unit and then passed to the computing unit until all operations are completed.

Challenges facing AO

The AO project faces some important challenges. Every network will eventually need to establish advantages in one or two verticals. For example, Arbitrum focuses on DeFi, Solana excels in Meme coins and DePIN, and IMX focuses on games. Arweave has been focusing on content storage, blockchain archiving, and oracle data permanence. AO is trying to redefine decentralized content and DeFi, especially in promoting the application of AI agents in the DeFi field.

1. DeFi adoption challenges

Although AO is committed to promoting the integration of DeFi and AI, the adoption of AI agents in the DeFi field has been slow, and no breakthrough applications have yet emerged. The closest attempt is to introduce machine learning models on-chain for yield optimization. However, these models are usually simple and are mainly used for yield prediction and strategy switching cost comparison. In contrast, large language models are highly nonlinear and non-deterministic, and still have difficulties in basic calculations.

2. Background of non-DeFi chains

Arweave is not a traditional DeFi public chain, and previous attempts to build DEX on it have failed. Therefore, AO needs to attract Arweave's existing community and new user groups. The token economics designed by the team reflects a deep understanding of this challenge, such as rewarding users who bridge DAI and stETH to attract funds. At present, AO's TVL has reached 578 million US dollars, and how to maintain the activity of these capitals is the key.

Token Economy and Participation Methods

After the mainnet launch in February 2025, anyone can contribute computing resources or deploy their own processes and agents. Cross-chain bridges will be open to support the transfer of any token to the AO network. As more people join and develop advanced AI or automated services, AO's decentralized and efficient architecture will unlock new possibilities in areas of trust and high computing power requirements.

Airdrop Mechanism

  • Total token supply and release plan: The total supply of AO tokens is 21 million, which will be halved at set intervals.
  • Airdrop qualifications: AR holders will receive AO airdrops according to their holding ratio; users who bridge DAI and stETH can also receive allocations.
  • Distribution method: Since February 27, 2024, 1.03 million AO tokens have been distributed to holders and bridgers; AR holdings are counted every 5 minutes to calculate the distribution ratio.

How to participate

  • Holding AR Tokens : One-third of the newly issued AO tokens are allocated to AR holders
  • Cross-chain transfer to DAI or stETH : Currently two-thirds of AO tokens are allocated to cross-chain users
  • Use AO mainnet application : multiple trading and lending platforms will be provided after the mainnet is launched
  • Providing computing resources : Anyone can contribute computing power to various processes on AO without permission

*Disclaimer: The author Arrington Capital is an early investor in Arweave and holds AR and AO tokens.

Market Opportunity
Hyperlane Logo
Hyperlane Price(HYPER)
$0.13257
$0.13257$0.13257
-1.61%
USD
Hyperlane (HYPER) Live Price Chart
Disclaimer: The articles reposted on this site are sourced from public platforms and are provided for informational purposes only. They do not necessarily reflect the views of MEXC. All rights remain with the original authors. If you believe any content infringes on third-party rights, please contact service@support.mexc.com for removal. MEXC makes no guarantees regarding the accuracy, completeness, or timeliness of the content and is not responsible for any actions taken based on the information provided. The content does not constitute financial, legal, or other professional advice, nor should it be considered a recommendation or endorsement by MEXC.

You May Also Like

South Korea Launches Innovative Stablecoin Initiative

South Korea Launches Innovative Stablecoin Initiative

The post South Korea Launches Innovative Stablecoin Initiative appeared on BitcoinEthereumNews.com. South Korea has witnessed a pivotal development in its cryptocurrency landscape with BDACS introducing the nation’s first won-backed stablecoin, KRW1, built on the Avalanche network. This stablecoin is anchored by won assets stored at Woori Bank in a 1:1 ratio, ensuring high security. Continue Reading:South Korea Launches Innovative Stablecoin Initiative Source: https://en.bitcoinhaber.net/south-korea-launches-innovative-stablecoin-initiative
Share
BitcoinEthereumNews2025/09/18 17:54
Trump Cancels Tech, AI Trade Negotiations With The UK

Trump Cancels Tech, AI Trade Negotiations With The UK

The US pauses a $41B UK tech and AI deal as trade talks stall, with disputes over food standards, market access, and rules abroad.   The US has frozen a major tech
Share
LiveBitcoinNews2025/12/17 01:00
Summarize Any Stock’s Earnings Call in Seconds Using FMP API

Summarize Any Stock’s Earnings Call in Seconds Using FMP API

Turn lengthy earnings call transcripts into one-page insights using the Financial Modeling Prep APIPhoto by Bich Tran Earnings calls are packed with insights. They tell you how a company performed, what management expects in the future, and what analysts are worried about. The challenge is that these transcripts often stretch across dozens of pages, making it tough to separate the key takeaways from the noise. With the right tools, you don’t need to spend hours reading every line. By combining the Financial Modeling Prep (FMP) API with Groq’s lightning-fast LLMs, you can transform any earnings call into a concise summary in seconds. The FMP API provides reliable access to complete transcripts, while Groq handles the heavy lifting of distilling them into clear, actionable highlights. In this article, we’ll build a Python workflow that brings these two together. You’ll see how to fetch transcripts for any stock, prepare the text, and instantly generate a one-page summary. Whether you’re tracking Apple, NVIDIA, or your favorite growth stock, the process works the same — fast, accurate, and ready whenever you are. Fetching Earnings Transcripts with FMP API The first step is to pull the raw transcript data. FMP makes this simple with dedicated endpoints for earnings calls. If you want the latest transcripts across the market, you can use the stable endpoint /stable/earning-call-transcript-latest. For a specific stock, the v3 endpoint lets you request transcripts by symbol, quarter, and year using the pattern: https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={q}&year={y}&apikey=YOUR_API_KEY here’s how you can fetch NVIDIA’s transcript for a given quarter: import requestsAPI_KEY = "your_api_key"symbol = "NVDA"quarter = 2year = 2024url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={API_KEY}"response = requests.get(url)data = response.json()# Inspect the keysprint(data.keys())# Access transcript contentif "content" in data[0]: transcript_text = data[0]["content"] print(transcript_text[:500]) # preview first 500 characters The response typically includes details like the company symbol, quarter, year, and the full transcript text. If you aren’t sure which quarter to query, the “latest transcripts” endpoint is the quickest way to always stay up to date. Cleaning and Preparing Transcript Data Raw transcripts from the API often include long paragraphs, speaker tags, and formatting artifacts. Before sending them to an LLM, it helps to organize the text into a cleaner structure. Most transcripts follow a pattern: prepared remarks from executives first, followed by a Q&A session with analysts. Separating these sections gives better control when prompting the model. In Python, you can parse the transcript and strip out unnecessary characters. A simple way is to split by markers such as “Operator” or “Question-and-Answer.” Once separated, you can create two blocks — Prepared Remarks and Q&A — that will later be summarized independently. This ensures the model handles each section within context and avoids missing important details. Here’s a small example of how you might start preparing the data: import re# Example: using the transcript_text we fetched earliertext = transcript_text# Remove extra spaces and line breaksclean_text = re.sub(r'\s+', ' ', text).strip()# Split sections (this is a heuristic; real-world transcripts vary slightly)if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1)else: prepared, qna = clean_text, ""print("Prepared Remarks Preview:\n", prepared[:500])print("\nQ&A Preview:\n", qna[:500]) With the transcript cleaned and divided, you’re ready to feed it into Groq’s LLM. Chunking may be necessary if the text is very long. A good approach is to break it into segments of a few thousand tokens, summarize each part, and then merge the summaries in a final pass. Summarizing with Groq LLM Now that the transcript is clean and split into Prepared Remarks and Q&A, we’ll use Groq to generate a crisp one-pager. The idea is simple: summarize each section separately (for focus and accuracy), then synthesize a final brief. Prompt design (concise and factual) Use a short, repeatable template that pushes for neutral, investor-ready language: You are an equity research analyst. Summarize the following earnings call sectionfor {symbol} ({quarter} {year}). Be factual and concise.Return:1) TL;DR (3–5 bullets)2) Results vs. guidance (what improved/worsened)3) Forward outlook (specific statements)4) Risks / watch-outs5) Q&A takeaways (if present)Text:<<<{section_text}>>> Python: calling Groq and getting a clean summary Groq provides an OpenAI-compatible API. Set your GROQ_API_KEY and pick a fast, high-quality model (e.g., a Llama-3.1 70B variant). We’ll write a helper to summarize any text block, then run it for both sections and merge. import osimport textwrapimport requestsGROQ_API_KEY = os.environ.get("GROQ_API_KEY") or "your_groq_api_key"GROQ_BASE_URL = "https://api.groq.com/openai/v1" # OpenAI-compatibleMODEL = "llama-3.1-70b" # choose your preferred Groq modeldef call_groq(prompt, temperature=0.2, max_tokens=1200): url = f"{GROQ_BASE_URL}/chat/completions" headers = { "Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json", } payload = { "model": MODEL, "messages": [ {"role": "system", "content": "You are a precise, neutral equity research analyst."}, {"role": "user", "content": prompt}, ], "temperature": temperature, "max_tokens": max_tokens, } r = requests.post(url, headers=headers, json=payload, timeout=60) r.raise_for_status() return r.json()["choices"][0]["message"]["content"].strip()def build_prompt(section_text, symbol, quarter, year): template = """ You are an equity research analyst. Summarize the following earnings call section for {symbol} ({quarter} {year}). Be factual and concise. Return: 1) TL;DR (3–5 bullets) 2) Results vs. guidance (what improved/worsened) 3) Forward outlook (specific statements) 4) Risks / watch-outs 5) Q&A takeaways (if present) Text: <<< {section_text} >>> """ return textwrap.dedent(template).format( symbol=symbol, quarter=quarter, year=year, section_text=section_text )def summarize_section(section_text, symbol="NVDA", quarter="Q2", year="2024"): if not section_text or section_text.strip() == "": return "(No content found for this section.)" prompt = build_prompt(section_text, symbol, quarter, year) return call_groq(prompt)# Example usage with the cleaned splits from Section 3prepared_summary = summarize_section(prepared, symbol="NVDA", quarter="Q2", year="2024")qna_summary = summarize_section(qna, symbol="NVDA", quarter="Q2", year="2024")final_one_pager = f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks — Key Points{prepared_summary}## Q&A Highlights{qna_summary}""".strip()print(final_one_pager[:1200]) # preview Tips that keep quality high: Keep temperature low (≈0.2) for factual tone. If a section is extremely long, chunk at ~5–8k tokens, summarize each chunk with the same prompt, then ask the model to merge chunk summaries into one section summary before producing the final one-pager. If you also fetched headline numbers (EPS/revenue, guidance) earlier, prepend them to the prompt as brief context to help the model anchor on the right outcomes. Building the End-to-End Pipeline At this point, we have all the building blocks: the FMP API to fetch transcripts, a cleaning step to structure the data, and Groq LLM to generate concise summaries. The final step is to connect everything into a single workflow that can take any ticker and return a one-page earnings call summary. The flow looks like this: Input a stock ticker (for example, NVDA). Use FMP to fetch the latest transcript. Clean and split the text into Prepared Remarks and Q&A. Send each section to Groq for summarization. Merge the outputs into a neatly formatted earnings one-pager. Here’s how it comes together in Python: def summarize_earnings_call(symbol, quarter, year, api_key, groq_key): # Step 1: Fetch transcript from FMP url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={api_key}" resp = requests.get(url) resp.raise_for_status() data = resp.json() if not data or "content" not in data[0]: return f"No transcript found for {symbol} {quarter} {year}" text = data[0]["content"] # Step 2: Clean and split clean_text = re.sub(r'\s+', ' ', text).strip() if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1) else: prepared, qna = clean_text, "" # Step 3: Summarize with Groq prepared_summary = summarize_section(prepared, symbol, quarter, year) qna_summary = summarize_section(qna, symbol, quarter, year) # Step 4: Merge into final one-pager return f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks{prepared_summary}## Q&A Highlights{qna_summary}""".strip()# Example runprint(summarize_earnings_call("NVDA", 2, 2024, API_KEY, GROQ_API_KEY)) With this setup, generating a summary becomes as simple as calling one function with a ticker and date. You can run it inside a notebook, integrate it into a research workflow, or even schedule it to trigger after each new earnings release. Free Stock Market API and Financial Statements API... Conclusion Earnings calls no longer need to feel overwhelming. With the Financial Modeling Prep API, you can instantly access any company’s transcript, and with Groq LLM, you can turn that raw text into a sharp, actionable summary in seconds. This pipeline saves hours of reading and ensures you never miss the key results, guidance, or risks hidden in lengthy remarks. Whether you track tech giants like NVIDIA or smaller growth stocks, the process is the same — fast, reliable, and powered by the flexibility of FMP’s data. Summarize Any Stock’s Earnings Call in Seconds Using FMP API was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story
Share
Medium2025/09/18 14:40