tweet / @hwchase17 / Apr 23 / failed
Chronological feed of everything captured from Harrison Chase.
how to deploy long horizon agents, and all the infra you need!
LangSmith now supports cron jobs as part of its deployments, enabling scheduled, fully asynchronous agent workflows. This addresses the need for cron-style scheduling similar to Upstash, Vercel, and Convex, but optimized for agentic processes. Documentation confirms integration for recurring tasks.
Harrison Chase's X (Twitter) feed is perceived as too pre-packaged in an hourly poll context. This user note flags the content as lacking authenticity or spontaneity. The feedback highlights a preference for less curated, more raw posting styles.
Harrison Chase's X feed is subject to an hourly poll monitoring its activity. The content features a user note highlighting this poll alongside the incomplete phrase "If they don’t…". This suggests potential anticipation of an announcement or condition dependent on external action.
Open-source models like GLM-5.1 have rapidly progressed to match frontier closed-source performance for most production use cases, particularly in inference efficiency. Lindy, where inference dominates costs over payroll, reports OSS inference costs now viable at 2-5x reductions. Multiple users confirm adopting GLM-5 as a daily driver, signaling a tipping point in OSS viability.
Deepagents Deploy now supports user-scoped memory via a user/ directory, providing each user a personalized writable AGENTS.md file. This file seeds on first deploy and persists across conversations, allowing agents to learn and retain user preferences at scale. Critical for production deployment beyond toy agents, it simplifies implementing persistent, user-specific state without custom infrastructure.
DeepAgents now supports structured outputs for subagents, allowing developers to define exact structured and validated data returned to the main agent. This addresses a key challenge in context engineering by clarifying communication protocols between subagents and the main agent. Documentation is available for implementation in LangChain OSS.
LangChain has introduced subagent support in deepagents deploy, enabling developers to add an agents/ directory with AGENTS.md files for each specialized subagent. Subagents facilitate task delegation using isolated and optimized context management. This update makes deepagents deployment more powerful for complex agent hierarchies.
Harrison Chase recommends using DeepAgents with its integrated memory for processing or tracking his X feed in an hourly poll context. This suggests DeepAgents as a straightforward tool for agentic workflows requiring persistent state. No additional memory implementation is needed when leveraging DeepAgents.
Harrison Chase, known for his X feed activity, is actively building a software harness. This development is highlighted in an hourly poll tracking his posts. The project signals ongoing technical work in his contributions.
Harrison Chase proposes implementing an hourly poll feature focused on his X feed. The request is brief and directly expressed as a desire to observe this functionality. No technical details or implementation specifics are provided in the content.
The PROMPTEVALS dataset provides a resource for developing and evaluating assertions and guardrails in production LLM pipelines. It contains over 2000 prompts and 12000 assertion criteria, enabling research into LLM reliability and alignment. The research demonstrates that fine-tuned open-source models can surpass proprietary LLMs like GPT-4o in generating relevant assertions, highlighting the potential for improved performance and efficiency.
Elixir implementation of a LangChain style framework that lets Elixir projects integrate with and leverage LLMs.. Stars: 1141
Helper library for LangSmith that provides an interface to run evaluations by simply writing config files.. Stars: 31
✨ AI-powered markdown editor - leverage LLMs with your documents - 100% local or in the cloud. Stars: 1327
RAI is a vendor agnostic agentic framework for Physical AI robotics, utilizing ROS 2 tools to perform complex actions, defined scenarios, free interface execution, log summaries, voice interaction and more.. Stars: 488
langgraph-supervisor-py. Stars: 1547
Agent Framework For Fintech and Banks. Stars: 7825
Data infrastructure for AI. Stars: 27391
50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.. Stars: 21177
An index of the LangChain + LangGraph ecosystem: concepts, projects, tools, templates, and guides for LLM & multi-agent apps.. Stars: 1694
Convert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website to learn more about our enterprise grade Platform product for production grade workflows, partitioning, enrichments, chunking and embedding.. Stars: 14441
The fullstack MCP framework to develop MCP Apps for ChatGPT / Claude & MCP Servers for AI Agents.. Stars: 9763
Global consulting firms are executing a massive strategic pivot from labor-intensive models to AI-augmented delivery, characterized by multi-billion dollar investments and the development of proprietary platforms (e.g., EY.ai, watsonx, Lilli). The industry is moving beyond simple use cases toward 'Agentic AI' and autonomous business processes, while simultaneously aggressive upskilling and acquiring AI-native boutiques to mitigate the risk of commoditization. Financial data indicates AI is already a significant revenue driver, with some firms seeing AI services constitute up to 20% of total revenue.
Major international consulting firms have collectively invested over $25 billion in AI capabilities since 2023, establishing AI as their primary growth driver. This shift extends beyond experimental initiatives to core business transformation, generating billions in AI-specific revenue. Firms are rapidly developing proprietary platforms and workforce skills to dominate the emerging agentic AI market, projected to reach $990 billion by 2027.
Harrison Chase, via an hourly poll on X, indicates that a particular, unnamed process is cheaper and faster, while also yielding good results. The specifics of the process are not detailed, limiting further technical analysis beyond the stated benefits.
GLM-5.1 is now available within Deep Agents, succeeding GLM-5. While the original post suggests open-weight models are gaining traction, a user
LangSmith Fleet has integrated with Arcade.dev, granting users access to over 8,000 tools. This integration allows for the streamlined creation of no-code agents, enabling functionalities similar to Claude Cowork or OpenClaw directly within LangSmith Fleet.
LangSmith focuses on observability for AI agents, specifically addressing the unpredictability of agent behavior outside of controlled demos. The platform enables developers to trace agent actions, evaluate performance, and iterate on fixes. This workflow aims to improve agent reliability and performance by providing concrete data on their operation.
LangChain’s agent middleware offers a powerful mechanism for tailoring agentic AI systems to specific use cases. This capability allows developers to modify agent behavior and integrate custom logic, significantly enhancing the flexibility and applicability of LangChain-based applications. The development of a community middleware page aims to foster collaboration and share best practices among users.
The content indicates a positive sentiment towards Harrison Chase's X feed, with a user explicitly agreeing with its content. This suggests that the feed is likely providing valuable or agreeable information to its audience, at least to this specific user. The short nature of the content limits further deep analysis regarding the specific topics or reasons for agreement.
This analysis explores the nuanced role of humor within technical communication, specifically referencing a poll on the Harrison Chase X feed. It distills the implicit insights on how humor can delineate technical sophistication or conversational tone depending on its stylistic application. The core insight suggests that understanding the "difference" in comedic approaches is crucial for effective audience engagement.
The emergence of LLM agents reduces the necessity of distributing specific codebases or applications. Instead, users can share 'idea files'—abstract, high-level specifications—that allow a recipient's agent to customize and build a tailored implementation. This transitions the primary unit of software sharing from executable code to conceptual frameworks.
The provided content is a user note regarding an "Hourly poll" on the "Harrison Chase X feed," followed by the positive sentiment "Nice." This content is extremely minimal and lacks substantive information for technical analysis or knowledge extraction beyond its literal interpretation. There are no detailed insights, data, or complex claims to synthesize.
Due to limited information, a comprehensive analysis of Harrison Chase's X feed is not currently possible. The provided content explicitly states a need for further investigation before any conclusions can be drawn, indicating an early stage of assessment.