Dataline Channel
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Dataline (Prev. Tearline) β the crypto data layer for AI agents. Natural-language in, source-attributed responses out. Cross-chain in one schema. Web: https://www.dataline.xyz/ Chat Group: https://t.me/datalinexyz Business: @DatalineAI
Show moreThe country is not specifiedCryptocurrencies171
π Analytical overview of Telegram channel Dataline Channel
Channel Dataline Channel (@dataline_news) in the English language segment is an active participant. Currently, the community unites 766 950 subscribers, ranking 171 in the Cryptocurrencies category.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 766 950 subscribers.
According to the latest data from 16 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -38 565 over the last 30 days and by -1 324 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 0.34%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
- Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 0 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βDataline (Prev. Tearline) β the crypto data layer for AI agents. Natural-language in, source-attributed responses out. Cross-chain in one schema.
Web: https://www.dataline.xyz/
Chat Group: https://t.me/datalinexyz
Business: @DatalineAIβ
Thanks to the high frequency of updates (latest data received on 17 September, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Cryptocurrencies category.
766 950
Subscribers
-1 32424 hours
-8 5697 days
-38 56530 days
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Channel Posts
Repost from N/a
Dataline x Base
Agents need data they can trust, not just fetch.
Now Base Builders get free access to Dataline's AI native Intelligence Layer: onchain intelligence with AI assisted confidence scores, built for autonomous agents.
Stop stitching APIs. Start shipping agents.
| 2 |  We are welcoming @ClusterProtocol to the Dataline Launch Partner cohort.
Cluster is the orchestration layer for autonomous AI on Base: serverless inference across 500+ models, GPU compute, and a tokenized data marketplace under one API, with private, verifiable inference. CodeXero, its browser-native IDE, ships dApps and agents from a single prompt.
CodeXero scaffolds the dApp from a prompt. Cluster runs the agents inside it on private, verifiable compute. Dataline is the live cross-venue read those agents call before they price, bid, or settle. | 2 453 |
| 3 |  Watch what our Market Data skill can do.
This 36-second demo walks through installing the Market Data skill in any Claude or Cursor session, asking the agent about Strait of Hormuz prediction markets on Polymarket, and getting back a structured analysis that combines odds, on-chain volume, and broader market context.
One skill. Spot, perp, prediction markets in one call.
Get yours today at:
https://www.dataline.xyz/mcp | 2 955 |
| 4 |  We are welcoming @NebulaiHQ to the Dataline Launch Partner cohort.
Nebulai is OpenCompute infrastructure for AI. An open network where anyone can contribute GPU compute in one click, and any team building AI can rent it on demand.
Nebulai brings the compute. Dataline brings the read. Any AI agent training or running inference on Nebulai that touches crypto reaches for Dataline for cross-venue prices, perp funding, and prediction market signal. | 3 202 |
| 5 |  We are welcoming @primus_labs to the Dataline Launch Partner cohort.
Primus is the trust and privacy layer for the agent economy. zkTLS proves Web2 data is real onchain. zkFHE lets agents compute over encrypted inputs. Together, they make the world's data useful onchain.
Agents need two things before they act: fresh data, and proof it's real. Dataline ships the read, live cross-venue prices, perp funding, prediction signal. Primus ships the proof, cryptographic attestation that the data wasn't tampered with. | 3 107 |
| 6 |  We are welcoming @SaharaAI to the Dataline Launch Partner cohort.
@HeySorinAI, their autonomous trading agent, runs in live markets. That requires real-time market data: spot, perp funding, prediction markets, available in one structured call. That is what Dataline provides. | 3 535 |
| 7 |  We are welcoming @FractionAI_xyz to the Dataline Launch Partner cohort.
Fraction AI allows anyone to create agents that move onchain assets across crypto. From yield to trading, agents route capital to the best opportunity available right now.
Cross-protocol allocation needs cross-venue grounding. Dataline ships that read: live stablecoin pricing, perp funding, prediction-market signal. One call before each rebalance. | 3 721 |
| 8 |  We are welcoming @SentientAGI to the Dataline Launch Partner cohort.
Sentient builds the OML stack (Open Monetizable Loyal models) and ROMA, a multi-agent orchestrator with Atomizer, Planner, Executor, Aggregator, and Verifier roles working in a loop.
When ROMA's Executor needs crypto data to act on, Dataline is the call. Spot, perp funding, prediction markets in one structured response, ready for the next loop iteration.
Read more: https://sentient.foundation | 3 900 |
| 9 | this logo doc hasn't updated the dataline font. did you update only the central server side?
http://100.77.177.14:3000/client/reports/dataline-xyz-logo-gallery?from=library | 1 |
| 10 |  We are welcoming @GoKiteAI to the Dataline Launch Partner cohort.
Kite Chain settles agent transactions in sub-second finality. The pipeline reads market state, makes a decision, settles the trade, all in the time other chains spend confirming a block.
Dataline is the data layer those agents call before they settle. Spot, perp funding, prediction markets, in one structured response. | 3 363 |
| 11 |  We are welcoming @b3dotfun to the Dataline Launch Partner cohort.
B3OS is the execution plane for crypto AI agents. Workflows, nodes, and connectors that run set-and-forget, built by the team that came from Coinbase Base.
Dataline is the data layer those agents query before they execute. Spot, perp funding, prediction markets, ready in a single structured call.
Read more:
https://x.com/b3dotfun/status/2062587654152282241 | 3 702 |
| 12 |  We're opening launch-partner slots ahead of public launch.**
Two partner types:
1οΈβ£Power Data Users. Agents that need structured crypto + on-chain data (trading, betting, research).
2οΈβ£Power Data Providers. DEXs, prediction markets, indexers, news / social feeds.
What the layer covers right now: Hyperliquid, Binance, OKX, Coinbase, Polymarket, Kalshi today; Bybit, dYdX, Pyth in integration. One natural-language request β one structured response with a 0-to-1 confidence score.
Production scale already: 19.4M+ on-chain tx, 96.4% execution success rate. BNB Chain Β· Sui Β· TON. ChatPilot, GhostDriver, FlowAgent running on top.
What launch partners get: co-publish week (joint blog + X thread + newsletter to 334K), direct eng support during integration, logo on dataline.xyz at launch, permanent registry listing.
Ask: apply via the partner signup form. API key, feed access, or source-attribution agreement after that.
Slots are limited. Sign up: gtm.dataline.xyz/partner-signup | 3 313 |
| 13 |  Building a prediction-market agent? Polymarket went dark in India last week. Indian ISPs cut access after a government betting-platform directive, and Kalshi could be next. If your agent only reads Polymarket's API, it has no graceful fallback today.
Dataline returns one response across both venues with source IDs, freshness per source, and divergence_flag inline. When a venue disappears or a competitor takes share, the schema absorbs the change two layers down. Whether Polymarket comes back online or Kalshi extends its lead, the agent code stays.
More details on using one schema for both markets
https://www.dataline.xyz/blog/aggregate-prediction-market-data | 3 538 |
| 14 |  Happy bitcoin pizza day! π€
Eating pizza tonight. Paying in dollars. The BTC stays in cold storage where it belongs. | 3 529 |
| 15 |  When should your trading agent call Polymarket's native API instead of Dataline?
When you need every CLOB tick, you're running market-making, or you have the engineering bench to maintain a GraphQL client through every schema change. That's a quant-firm workflow. The native API returns raw GraphQL with nested condition arrays, prices, and volume snapshots. That works when your own code does the math. It breaks down when your model has to read the response and explain it.
Use Dataline if your agent has to cite its answers. Every response ships with source_id, freshness_seconds, and divergence_flag inline, so your model spends ~250 tokens where the raw GraphQL payload would burn ~1,800. Coverage spans Polymarket, Hyperliquid, EVM, Solana, and Sui on one schema, with cross-source divergence flagged the moment Polymarket disagrees with on-chain options pricing. Full trade-offs, latency numbers, and code samples: dataline.xyz/blog/polymarket-api-for-ai-agents. | 4 213 |
| 16 |  What our mark shows you. 5 roots at the bottom = data sources. Crossing branches up through one trunk = one Dataline call. 18 leaves at the top = the agent decisions you unlock. The 2 red leaves = divergence flags when sources disagree.
The mark is the product. Every call queries six (or six hundred) crypto markets in one shot and returns one structured response your autonomous agent can act on. The tree IS the data lifeline.
Markets usually agree. Dataline ships that agreement as a single signal β perps, on-chain state, probability, metadata aligned, with one confidence score. Your agent acts on it immediately instead of stitching together four feeds and reconciling shapes. When markets diverge (price splits, oracle drift, hedged positions), Dataline returns the divergence inline with source IDs and freshness flags.
One contract per market. Your agent stops writing per-source adapters and trades on the data lifeline. Read more: dataline.xyz. | 3 977 |
| 17 |  Tearline is now Dataline. Same team, same product surface (ChatPilot, GhostDriver, FlowAgent). The new name calls out the job: the data lifeline an autonomous agent runs on.
Tearline started as the chat surface. The thing the team actually built underneath was a data layer that shapes crypto markets into responses an agent can parse, cite, and act on. Dataline names the layer customers pay for. Production scale, already live: 19.4M+ on-chain transactions processed, 96.4% execution success rate, coverage across BNB Chain / Sui / TON, 2.5M+ agent interactions through ChatPilot.
If your agent has to act on crypto data, this is the layer to wire into. Full breakdown: dataline.xyz/blog/tearline-to-dataline.
Stay tuned for more exciting news to come! | 5 297 |
| 18 |  What is Clawbot?
Plan β Execute β Observe β Iterate.
After two years of building, Tearline now supports the full loop.
Tearline = Web3 Clawbot. | 4 442 |
| 19 |  +1We were honored to take part in AdoptAI in Paris, an event held under the High Patronage of President Emmanuel Macron β where the next era of AI was mapped out. π«π·β¨
Our biggest takeaway?
Weβre entering a world where AI Agents donβt just think; they trade.
Data becomes currency. Every byte has a price. Every action triggers a micro-payment. β‘οΈ
And only crypto can power this new machine economy: permissionless, programmable, ultra-fast value flows at planetary scale. ππΈ
The shift from a human-driven economy to an AI-driven one has already begun.
And Tearline is building the rails for it. π | 4 798 |
| 20 | π Some good news worth revisiting β featured on Yahoo Finance! π
A while back, Tearline officially joined the Google for Startups Cloud Program β a major milestone in our Full-Chain AI journey. π‘
Through this partnership, weβre leveraging Google Cloudβs AI-optimized infrastructure, training resources, and $200K in cloud credits to accelerate innovation and redefine whatβs possible for AI-native Web3 systems.
Since then, weβve been putting these resources to work β scaling our agent ecosystem, expanding use cases, and powering new layers of AI automation across Web3.
Read the full coverage on Yahoo Finance π
http://bit.ly/4h6NcnE | 4 797 |
