FactorAI

CA: 0xB201eAd1771c07cfE79cAde35550c702a70f7777
FactorAI is a workspace for coordinated AI agents.🤖

[FactorAI @factoraiwork]

$CA: 0xB201eAd1771c07cfE79cAde35550c702a70f7777
Site: https://t.co/LEvrpUVfms

❤️‍🔥❤️‍🔥❤️‍🔥
$FTAI is live on BNB Smart Chain.
💰$FTAI💰 is a Binance-based token associated with the FactorAI project-an AI workspace focused on coordinated agents, parallel workflows, and human-controlled automation.

🚨Beware of copycats. 🚨
Only trust the contract address announced by this account.

[FactorAI @factoraiwork]

$CA: 0xB201eAd1771c07cfE79cAde35550c702a70f7777
Site: https://t.co/37vaHvl6PT
we've updated the website to fix some issues, and we're continuously working on improving different parts of the platform. there's still a lot ahead of us, and we believe FactorAI has great potential to grow even further. 🚀

[FactorAI @factoraiwork]

Follow the FactorAI build: coordinated agents, scheduled workflows and human oversight.
The product focus is concrete—connect context, organize execution, inspect results and define the boundaries before agents act.
$FTAI is launching on BNB Chain. Follow the official channels below for product and launch updates.
Website: https://t.co/EXFt9upifL
X: https://t.co/Icdyi9GhUP
Telegram: https://t.co/zO7gTJPig5
$CA: 0xB201eAd1771c07cfE79cAde35550c702a70f7777

[FactorAI @factoraiwork]

The builders behind AI agents need programmable access, too.
FactorAI's developer interfaces span a versioned REST API, a native CLI and a Streamable HTTP MCP server.
Public discovery exposes the command catalog; execution requires scoped API keys that define integration capabilities.
Request IDs, stable error codes and rate-limit information help make automated clients easier to inspect and operate.
Bring workflow coordination into your developer stack. $FTAI | https://t.co/EXFt9upifL

[FactorAI @factoraiwork]

A relevant reply starts with understanding the person and the signal.
FactorAI's sales workflow examples research public requests, check available contact context and prepare reply drafts for review.
The draft-first approach leaves room to inspect the evidence, adjust the message and decide whether outreach makes sense.
Use connected account history to avoid repeating contact where the workflow has the necessary access.
Build useful conversations with a human in the loop. $FTAI | https://t.co/EXFt9upifL
$CA: 0xB201eAd1771c07cfE79cAde35550c702a70f7777

[FactorAI @factoraiwork]

$CA: 0xB201eAd1771c07cfE79cAde35550c702a70f7777
We’ve completed the DEX Screener payment for $FACTORAI.

Once the profile update goes live, you’ll be able to find FACTOR AI’s official website and community links directly on the token page.

We’re also continuously updating the project in detail, putting our hearts into every part of it and making sure everything is properly presented.

We’ll share the updated profile here as soon as it’s available. ❤️
https://t.co/mSAqDBASZA

[FactorAI @factoraiwork]

RT @factoraiwork: We’ve completed the DEX Screener payment for $FACTORAI.

Once the profile update goes live, you’ll be able to find FACTOR…

[FactorAI @factoraiwork]

$CA: 0xB201eAd1771c07cfE79cAde35550c702a70f7777
CT moves fast. Research still needs receipts.
Use FactorAI to coordinate source gathering, comparison, drafting and review around a clear research objective.
Keep findings alongside their sources and activity history so people can inspect the basis for the result.
A recurring workflow gives you a process to revisit; the evidence gives you something concrete to question.
Useful research earns attention through its sources. $FTAI | https://t.co/EXFt9upifL

[FactorAI @factoraiwork]

Builders: the useful engineering output is a change you can inspect.
FactorAI's engineering workflows focus on investigating failures, tracing relevant code and preparing draft fixes with tests.
The review step matters: inspect the patch and the execution evidence before deciding whether to merge.
Actual test coverage depends on the available sources and runtime; a proposed fix still needs verification.
Bring agents into the workflow. Keep engineering judgment in it. $FTAI | https://t.co/EXFt9upifL

[FactorAI @factoraiwork]

An agent needs more than a prompt when the work spans your stack.
FactorAI brings connected tools, useful sources and model providers into a shared project context.
That can support workflows involving code, documents, mail, search and operational data, subject to the connections you enable.
Choose the accounts and permissions deliberately, then review what each workflow reads, prepares and asks you to approve.
Give the agents useful context. Keep control of the operation. $FTAI | https://t.co/EXFt9upifL
$CA: 0xB201eAd1771c07cfE79cAde35550c702a70f7777

[FactorAI @factoraiwork]

@sweet919268 just for minutes, we will do so that

[FactorAI @factoraiwork]

💸💸💸
We’re preparing the DEX Screener payment for $FTAI. Once the profile update is live, traders will have a clearer way to find FacorAI official website and community links directly from the token page.

We’ll share the updated profile here as soon as it’s available. 🤖🤖🤖

[FactorAI @factoraiwork]

Your AI workflow should fit the model setup you choose.
FactorAI supports connecting provider keys and choosing an execution harness for your work.
Supported options include OpenAI, xAI and OpenRouter keys, with runtimes such as Claude Code, Codex and the built-in runtime.
Separate choices about models, execution and permissions so the operation reflects how your team wants to build.
For builders who care about their stack. $FTAI | https://t.co/EXFt9upifL

[FactorAI @factoraiwork]

💸💸💸
We’re preparing the DEX Screener payment for $FTAI. Once the profile update is live, traders will have a clearer way to find FacorAI official website and community links directly from the token page.

We’ll share the updated profile here as soon as it’s available. 🤖🤖🤖

[FactorAI @factoraiwork]

$CA: 0xB201eAd1771c07cfE79cAde35550c702a70f7777
Site: https://t.co/LEvrpUVfms

❤️‍🔥❤️‍🔥❤️‍🔥
$FTAI is live on BNB Smart Chain.
💰$FTAI💰 is a Binance-based token associated with the FactorAI project-an AI workspace focused on coordinated agents, parallel workflows, and human-controlled automation.

🚨Beware of copycats. 🚨
Only trust the contract address announced by this account.

[FactorAI @factoraiwork]

A short question and a multi-step debugging task do not need the same model tier.
FactorAI's Auto routing selects efficient, fast or frontier tiers according to what each request asks for.
Simple work can stay on lighter tiers while harder planning or debugging requests move to stronger ones.
Prefer direct control? Select a model yourself; a connected provider also takes the request outside Auto routing.
Choose the compute the work needs.
$FTAI | https://t.co/EXFt9upifL

[FactorAI @factoraiwork]

Here’s what we’re planning to make $FTAI liquidity easier to verify on BNB Chain.
First, publish the pool address and initial reserves when the pool goes live.
Next, share the LP custody or lock details and link to the on-chain transaction if a lock is put in place.
Then track reserves and show price impact for example trade sizes, with fees and assumptions clearly labeled.
Which would you want in a liquidity update: reserve history, sample quotes, or LP lock details?
Proposed roadmap. Follow FactorAI → https://t.co/zO7gTJPig5

[FactorAI @factoraiwork]

Same starting price. Same swap. Different execution.
In this hypothetical example, a 20 BNB buy has a 10% average price premium in the smaller pool and 2.5% in a pool with 4× the reserves.
The curve explains why: as the swap adds BNB and removes FTAI, the price moves along x × y = k.
Compare the trade size with the pool’s reserves before reading the quote.
Illustration only. Fees are excluded. Follow $FTAI updates → https://t.co/Icdyi9GhUP

[FactorAI @factoraiwork]

$FTAI liquidity starts with two numbers: how much FTAI and how much BNB sit in the pool.
In an illustrative constant-product pool, spot price = BNB reserve ÷ FTAI reserve.
100,000 FTAI paired with 200 BNB implies 0.002 BNB per FTAI at that instant.
A deeper pool at the same price can absorb a larger swap with less price impact.
Which would you check first: reserve depth or the headline price?
Example only. Explore FactorAI → https://t.co/EXFt9upifL

$CA: 0xB201eAd1771c07cfE79cAde35550c702a70f7777