信使公司拥有发布，即Litterpress-1的β，一个编码agent,Hander和IDE用于生长工程。 Letterpress-1编码agent读取一个网站及其市场，然后决定应该存在什么。 它在沙盒中构建变化，并打开带有直播预览的拉动请求。
TL；数据
- 大小：字母组称，它结合了边疆模型和微调的小模型；名称和大小不披露。
- 运行在：信件主机平台与您的 git 存储器对齐。 无需本地GPU. 每个字母的设置需要不到5分钟。
- 性能：无公共基准测试。 字母显示 1 自报，侧页构建对 Codex.
最佳：通过MCP、REST和CLI的1个登记册曝光760个工具。
- 最糟的是：没有可计量的eval；入职计划为每月100美元，超龄率为5x。
莱特普斯1是什么？
Letterpress-1是一个来自The Letter Company的生长工程agent. 它将网站和市场信号转化为拉动请求。 信件公司团队认为，网站与app不同，react到外部势力。 其中包括agent行为，Google排名，人类访问和竞争者活动。 通用编码agents是用来缩放代码库的，而不是对这些信号采取行动的。
agent是针对在git中托管其营销网站的团队。 它具有主动性：它提出了下一步工作。 例如去匿名、登陆页、暖气电子邮件、博客自动化和agent遥测。
利特普斯1号如何工作？
Letterpress-1以3种模式运行：
- 构建：从一个提示创建一个页面或功能，品牌副本和视觉内容保持一致。
——计划：选择目标，获得增长行动的建设计划。
- 修复：找到现场问题并发送拉动请求来修复它们。
每一个变化都遵循同样的循环。 agent读取遗址，在沙盒中规划和建造。 它审查自己的变化，然后打开拉动请求。 每次更改都会返回您网站的预览链接。 在您选择预览、拉请求或检查合格后合并之前，获得将不作任何更改。
信队列出6个表面。 这些是网络平台，Slack应用，字母press CLI和一个网站门户。 一个独立的应用程序和你自己的agent环绕列表。 现场的IDE在直播页面上工作。 按 L 键，编辑您看到它的地方的文本，并随后发出拉动请求。 showcase实例还涵盖现场编辑的A/B测试。
对于开发者，agents.md文档MCP服务器在app.letterstory.com/api/mcp与OAuth. MCP Express, OpenAPI spec and API 参考格式 1 注册 760 工具。 Claude Code,Codex或任何MCP能力agent都可以调用应用程序使用的相同功能。
证据、模拟、座椅和幽灵是什么？
发布的帖子突出4个原始人
- 证明：客户成功的签名证据，由遥测和计费数据制成，供agent访客使用。
- 模拟：以模拟数据显示你的API或MCP的镜像版本，因此agents在购买前可以尝试。
- 座椅：自行托管，可复制的基准测试应用，带有开源代码和独立治理。
- 幽灵：用来沙盒生长策略的独立自主博客。
网站新增更多：Kernels,Retterbases，访客和邮件。 然后它观看竞争对手的现场地图，并标出新的页面。 信件小组将这些定位为G2,Semrush,Profound,RB2B和Instantly等工具的替换。
Letterpress-1与Codex,Claude Code和Cursor的对比如何？.
Letterpress-1要多少钱？
信帐单1 共享代币池。 按列表价格计算，一个符号等于1%。 计划是基本计划(100,10,000令牌)、启动器计划(500,50,000美元)、增长计划(1,000,130,000美元)和规模计划(2,500,400,000美元)。 企业每月以200万令牌的价位开始，价位为列表的一半。
Agent运行成本模型支出加20%，一般每个拉力请求150到900个令牌。 名单上是1.5美元到9美元。 一篇发表的文章花费了1,030个令牌。 在Basic和Starter上超龄为每1000个令牌50美元，5x为列表率。 未使用的符号不会翻转。
关键外卖
-Litterpress-1是一款编码agent，为生长工程建造的牵引和IDE.
- 每艘变换船作为沙箱拉力请求并附有预览链接
- 760个工具在MCP、REST和CLI上空暴露，用于外部agents。
- 定价开始为每月100美元；agent运行成本模式支出加20%。
——尚未公开基准测试；质量声称自报。
请检查access-date=中的日期值 (帮助) 产品页面，定价，展示，agents.md和X上的发布帖子。 并加入我们的通讯。 等等你在电报里吗现在你也可以加入我们的电报。
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米哈尔·萨特(Michal Sutter)是一名数据科学专业人员，拥有帕多瓦大学的数据科学硕士学位。 在统计分析、机器学习和数据工程方面有一个坚实的基础，Michal在将复杂的数据集转化为可操作的见解方面表现突出。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
The Letter Company has released the beta of Letterpress-1, a coding agent, harness and IDE for growth engineering. The Letterpress-1 coding agent reads a website and its market, then decides what should exist. It builds the change in a sandbox and opens a pull request with a live preview.
TL;DR
- Size: Letter team says it combines frontier models with fine-tuned small models; names and sizes are not disclosed.
- Runs on: Letter’s hosted platform against your git repository. No local GPU needed. Setup takes under 5 minutes, per Letter.
- Performance: No public benchmark. Letter shows 1 self-reported, side-by-side page build against Codex.
- Best: 760 tools exposed through 1 registry over MCP, REST and CLI.
- Worst: No measurable eval; entry plan is $100/month with overage at 5x the list rate.
What is Letterpress-1?
Letterpress-1 is a growth engineering agent from The Letter Company. It turns site and market signals into pull requests. The letter company team argues that websites, unlike apps, react to outside forces. These include agent behavior, Google rankings, human visits and competitor activity. General coding agents were built to scale codebases, not to act on those signals.
The agent is aimed at teams that host their marketing site in git. It is proactive: it suggests the next thing to build. Examples include de-anonymization, landing pages, warmed emails, blog automation and agent telemetry.
How does Letterpress-1 work?
Letterpress-1 runs in 3 modes:
- Build: create a page or feature from a prompt, with brand copy and visuals kept consistent.
- Plan: pick a goal and get a build plan of growth actions.
- Fix: find site issues and dispatch pull requests to fix them.
Every change follows the same loop. The agent reads the site, plans and builds in a sandbox. It reviews its own change, then opens a pull request. Each change returns a preview link of your own site. Nothing lands until you choose a preview, a pull request, or a merge after checks pass.
Letter team lists 6 surfaces. These are the web platform, a Slack app, the letterpress CLI and a website portal. A standalone app and your own agent round out the list. The in-site IDE works on a live page. Press ⌘L, edit text where you see it, and a pull request follows. Showcase examples also cover A/B tests from live edits.
For developers, agents.md documents an MCP server at app.letterstory.com/api/mcp with OAuth. The MCP manifest, OpenAPI spec and API reference form 1 registry of 760 tools. Claude Code, Codex or any MCP-capable agent can call the same functions the app uses.
What are Proofs, Simulations, Benches and Phantoms?
The launch post highlights 4 primitives aimed at winning over AI agents as buyers:
- Proofs: signed evidence of customer success, built from telemetry and billing data, for agent visitors.
- Simulations: mirror versions of your API or MCP with simulated data, so agents can try before buying.
- Benches: self-hosted, reproducible benchmark apps with open-source code and independent governance.
- Phantoms: independent, autonomous blogs used to sandbox growth strategies.
The site adds more: Kernels, Letterbases, Visitors and Mailers. Then it watches competitor sitemaps and flag new pages. Letter team positions these as replacements for tools like G2, Semrush, Profound, RB2B and Instantly.
How does Letterpress-1 compare with Codex, Claude Code and Cursor?
How much does Letterpress-1 cost?
Letter bills 1 shared token pool. A token equals 1 cent at list price. Plans are Basic ($100, 10,000 tokens), Starter ($500, 50,000), Growth ($1,000, 130,000) and Scale ($2,500, 400,000). Enterprise starts at 2 million tokens a month at half the list rate.
Agent runs cost model spend plus 20%, typically 150 to 900 tokens per pull request. That is $1.50 to $9 at list. A published article costs 1,030 tokens. Overage on Basic and Starter is $50 per 1,000 tokens, 5x the list rate. Unused tokens do not roll over.
Key Takeaways
- Letterpress-1 is a coding agent, harness and IDE built for growth engineering.
- Every change ships as a sandboxed pull request with a preview link.
- 760 tools are exposed over MCP, REST and CLI for external agents.
- Pricing starts at $100/month; agent runs cost model spend plus 20%.
- No public benchmark yet; quality claims are self-reported.
Check out the product page, pricing, showcase, agents.md and the launch post on X. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.
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