Hasty Briefsbeta

All tags

#ai agents

178 stories total

Bilingual

The Agentic Web and Original Sin
a year ago
- Advertising is considered the original sin of the web, shaping user expectations and content monetization.
- Marc Andreessen reflects on the inability to integrate payments into browsers, leading to advertising dominance.
- Advertising aligns incentives: users access free content, creators reach wider audiences, and advertisers find new customers.
- Micro-transactions for content are impractical due to credit card fees and human decision fatigue.
- Subscriptions succeed by focusing on regular, valuable content delivery rather than individual pieces.
- The ad-supported web is declining due to bad incentives and the rise of AI-driven content discovery.
- Google's role in sustaining the open web is questioned as user engagement shifts to walled gardens.
- Microsoft proposes an 'Open Agentic Web' with protocols like MCP and NLWeb to enhance AI-agent interactions.
- Stablecoins offer a viable solution for micro-transactions in the agentic web, enabling new content marketplaces.
- A future web could thrive with open protocols, aligned incentives, and native payments for AI-driven content.
Mistral Agents API
a year ago
- Mistral announces the new Agents API, enhancing AI capabilities for active problem-solving.
- The API combines Mistral's language models with built-in connectors for code execution, web search, image generation, and MCP tools.
- Features include persistent memory across conversations and agentic orchestration capabilities.
- Diverse applications across sectors: coding assistant with GitHub, Linear tickets assistant, financial analyst, travel assistant, and nutrition assistant.
- Built-in connectors include code execution, image generation, document library, and web search.
- MCP tools enable integration with external systems like APIs, databases, and user data.
- Stateful conversations maintain context and support streaming outputs for real-time interactions.
- Agent orchestration allows dynamic collaboration between multiple agents to solve complex problems.
- Developers can create workflows with agent handoffs for efficient task delegation.
Patched (YC S24) Is Hiring SWEs in Singapore
a year ago
- Open-source workflow automation for dev teams
- Looking for a high-energy, resourceful engineer to make AI agents production-ready
- Role involves creating accurate, reliable, and secure AI agents with autonomy
- No hard requirements, seeking exceptional software engineers
- Application requires a 1-minute video introduction
- Two rounds of interviews
- Patched is building open-source AI agents for enterprise teams
- Believes open-source is the future of AI for trust and transparency
- Working with first enterprise clients to iterate on product
An agent is an LLM wrecking its environment in a loop
a year ago
- Solomon Hykes defines an AI agent as 'an LLM wrecking its environment in a loop'.
- This definition combines the 'tools in a loop' concept (popularized by Anthropic) with the classic academic definition of agents acting in an environment.
- Agents can include worms, dogs, thermostats, airplanes, robots, humans, companies, and countries.
- Recent articles cover topics like prompting ChatGPT, LLM behavior (SnitchBench), and AI's impact on jobs.
Show HN: A "Course" as an MCP Server
a year ago
- Learn to build and deploy AI agents using Mastra, guided by an MCP Agent.
- Course includes 3 lessons with interactive progress, taking about 12 hours to complete.
- Set up development environment, test in a live playground, and deploy agents to production.
- Integrate external services like email, social media, and GitHub using MCP servers without custom code.
- Configure agent memory for conversation history, preferences, and personalized responses.
This AI Agent Should Have Been a SQL Query
a year ago
- AI Agents have evolved into sophisticated, autonomous systems.
- Streaming SQL queries can be used to build agentic systems, leveraging platforms like Apache Flink.
- Apache Flink offers benefits in consistency, scalability, and developer experience for AI Agents.
- AI Agents interact with LLMs, requiring context and memory for effective operation.
- Event-driven architectures are ideal for AI Agents, allowing real-time data processing.
- Flink SQL supports ML/AI model integration, enabling LLM interactions within queries.
- Retrieval-augmented generation (RAG) helps provide context to LLMs using vector databases.
- Process Table Functions (PTFs) in Flink SQL allow custom logic for agent memory and tool integration.
- Flink Agents sub-project (FLIP-531) aims to create a Flink-based runtime for AI Agents.
- SQL democratizes AI Agent development, making it accessible to SQL-savvy engineers.
From LLM to AI Agent: What's the Real Journey Behind AI System Development?
a year ago
- AI agents are popular but not always necessary; simpler solutions can be more cost-effective.
- Large Language Models (LLMs) are a snapshot of training data knowledge but lack real-time information without enhancements.
- Retrieval Augmented Generation (RAG) improves LLMs by providing relevant and current context.
- AI workflows automate structured tasks by connecting to APIs and following predefined steps.
- AI agents autonomously reason, make decisions, and manage complex processes like recruitment.
- Key takeaway: Start simple, add complexity as needed, and prioritize reliability over capability.
Show HN: Pickaxe – a TypeScript library for building AI agents
10 months ago
- Pickaxe is a Typescript library for building fault-tolerant and scalable AI agents.
- It handles durable execution, queueing, and scheduling, allowing focus on core business logic.
- Agents in Pickaxe are just functions, making integration with existing codebases easy.
- Supports building agents that call tools, other agents, or any user-defined functions.
- Features durable execution with automatic checkpoints for recovery from failures.
- Agents are code-first, distributed, configurable, and runnable on any container-based platform.
- Designed for massive throughput and parallelism, handling billions of tasks monthly.
- Not a framework; imposes no constraints on LLM calls, business logic, or agent design.
- Execution model similar to Temporal but with simplified workflow scheduling.
- Agents should be stateless reducers with no side effects for durable execution.
- Built on Hatchet, a durable task queue for resilient distributed systems.
- Contributions welcome, with a preference for discussions before large changes.
Introducing Qodo Gen CLI: Build and Run Coding Agents Anywhere in the SDLC
10 months ago
- Qodo Gen CLI is a command-line interface for building, managing, and running AI agents.
- Developers can create custom agents, automate workflows across the SDLC, and integrate AI capabilities into any IDE.
- Features include workflow-wide automation, terminal and browser interaction, full model flexibility, and flexible deployment options.
- Agents are configurable with triggers, inputs, actions, and results, defined using a simple .toml format.
- Examples of community-built agents include Accessibility Auditor, Batch Playwright Fixer, and Performance Optimizer.
- Qodo Gen CLI supports multiple execution modes: CI mode, Webhook mode, and MCP mode.
- Installation is simple with npm: `npm install -g @qodo/gen`.
- Future plans include Background Agents, Parallel Execution, and applying best practices across the SDLC.
Reinforcement learning, explained with a minimum of math and jargon
10 months ago
- Reinforcement learning (RL) is a key technique enabling AI agents to improve through trial and error, overcoming limitations of imitation learning (pretraining).
- Early AI agents like BabyAGI and AutoGPT failed due to compounding errors—small mistakes snowballing when models ventured 'out of distribution' from their training data.
- RL methods like DAgger and RLHF (Reinforcement Learning from Human Feedback) help models recover from errors by providing automated feedback, crucial for complex tasks like language modeling.
- Combining imitation learning (for initial training) and RL (for refinement) yields robust AI systems, as seen in self-driving tech (Waymo) and agentic AI tools (Claude 3.5, o1).
- Chain-of-thought reasoning, enhanced by RL, allows models like OpenAI’s o1 and DeepSeek’s R1 to solve multi-step problems by 'thinking' through extended token sequences.
- Modern AI agents (e.g., coding assistants, research tools) rely on RL to maintain focus across iterative tasks, a leap from brittle 2023 models.
- Constitutional AI and synthetic data (e.g., Claude 3.5 Opus judging Sonnet) bootstrap RL by using advanced models to train weaker ones.
Confidence in agentic AI: Why eval infrastructure must come first
10 months ago
- AI agents are being deployed to save human capital and transform business operations.
- Rocket Companies saw a 3x increase in website conversion rates using AI agents.
- An AI agent built in two days saved Rocket $1 million annually by automating mortgage underwriting tasks.
- AI agents saved Rocket over a million team member hours in 2024, allowing employees to focus on client needs.
- Team members at Rocket handled 50% more clients due to AI-driven efficiency gains.
- Engineering teams are shifting from deterministic software engineering to probabilistic AI approaches.
- LLMs have improved, making AI agents more predictable, but challenges remain in model orchestration and scalability.
- Scaling AI agents involves solving technical problems like latency and agent routing.
- Companies initially build AI agents in-house but struggle with maintenance and evolving infrastructure.
- Agentic AI complexity will grow, requiring robust checks, human oversight, and monitoring systems.
- Evaluating AI agents requires pre-built test infrastructure and continuous validation against benchmarks.
- Non-deterministic AI behavior necessitates large-scale simulation and scenario testing for reliability.
What to build instead of AI agents
10 months ago
- Hugo advises against starting with AI agents due to their complexity and frequent failures.
- Common issues with agents include forgetting tasks, incorrect tool selection, and coordination breakdowns.
- Five alternative workflow patterns are suggested: Prompt Chaining, Parallelization, Routing, Orchestrator-Worker, and Evaluator-Optimizer.
- Agents are best used in human-in-the-loop scenarios where oversight can correct mistakes.
- Agents should be avoided in stable enterprise systems and high-stakes decisions due to unpredictability.
- Key takeaways include the overhyped nature of agents and the importance of observability and explicit control.
(Experiment) Colocating agent instructions with eng docs
10 months ago
- The author experimented with embedding AI agent instructions within internal engineering documentation instead of maintaining separate agent docs.
- Instructions for AI agents were embedded as comments in the documentation file, which are invisible to human readers but accessible to agents.
- The experiment involved converting a code example into a buildable and testable example using the embedded instructions.
- Gemini CLI successfully followed the instructions, including creating a failing test first and then fixing it.
- The experiment revealed a mistake in the instructions, confirming that the agent was closely following them.
- The author notes that the experiment was informal and suggests that having both agent docs and targeted instructions might yield the best results.
- The process included updating build files for Bazel, GN, and CMake, and verifying that the documentation build still works.
Lessons to Get Started Building AI Agents
10 months ago
- 11 lessons covering AI Agent fundamentals
- Multi-language support available
- Includes code examples in the code_samples folder
- Uses Azure AI Foundry and GitHub Model Catalogs
- Features AI Agent frameworks and services from Microsoft
- Lessons include written content, videos, and Python code samples
- Community support via Discord and Developer Forum
- Encourages contributions with a Contributor License Agreement (CLA)
- Follows Microsoft Open Source Code of Conduct
- Contains trademarks subject to Microsoft's guidelines
WASM Agents: AI agents running in the browser
10 months ago
- Wasm-agents allow AI agents to run directly in a browser without extra dependencies.
- The project uses WebAssembly (Wasm) and Pyodide to execute Python code in browsers.
- Agents are packaged as standalone HTML files containing both UI and code.
- Supports OpenAI-compatible APIs, including self-hosted models like those from HuggingFace, vLLM, or Ollama.
- Demos include conversational agents, multi-agent systems, and tool-calling agents.
- Known limitations include dependency on the openai-agents framework, CORS issues, and hardware constraints for local models.
- Encourages customization, experimentation, and feedback from the community.
Why the simplest desktop agent abstraction wins
10 months ago
- Bytebot is designed with a simple and universal abstraction for agent control: keyboard, mouse, and screen.
- This approach mimics human remote workers, making it robust, generalizable, and future-proof.
- Previous attempts with browser agents faced limitations like unreliable drag-and-drop and lack of support for offline tasks.
- The 'Bitter Lesson' highlights that simple methods scaled with compute yield the biggest AI gains, influencing Bytebot's design.
- Bytebot's architecture enables universality, fidelity, composability, observability, and extensibility across all computing environments.
- Companies benefit from out-of-the-box functionality without needing custom integrations or API maintenance.
- Bytebot is built for long-term durability and generality, not just current LLM performance.
From AI to Agents to Agencies
10 months ago
- AI Agents are autonomous systems capable of independent problem-solving and task execution.
- Agencies represent the next evolution, orchestrating multiple specialized intelligences for a single task.
- Agencies differ from Agents by using multiple intelligences (LLMs) for different subtasks within one task.
- Core components of Agencies include Task Context Management, Intelligence Allocation System, and Orchestration Logic.
- Agencies improve efficiency, cost-effectiveness, and quality by matching the right intelligence to each subtask.
- The evolution path: AI (2020-2023) → Agents (2024-2025) → Agencies (2025+).
The MCP hype is a distraction. AI Agents should just build their own tools
10 months ago
- ToolGen introduced in rtrvr.ai v12.5 allows agents to create tools on-the-fly instead of using pre-defined ones.
- The browser serves as the authentication layer, eliminating the need for manual API key configurations.
- Example use case: Creating a HubSpot API tool from a single prompt to upload LinkedIn connections as contacts.
- rtrvr.ai dynamically generates and uses tools based on context, improving efficiency.
- New credit system updates include reduced costs for BYOK, generous referral bonuses, and free credits for all users.
AI Agents take control of our code
10 months ago
- AI Agents are becoming increasingly popular in software tools, especially in data science.
- MLJAR Studio introduces a built-in AI Agent that works inside notebooks, understanding and executing code locally.
- Three modes are available: Data Analyst Mode for no-code analysis, Code Assistant Mode for Python code generation, and Teacher Mode for interactive learning.
- The AI Agent adjusts responses based on the notebook's current state, including variables and prior cells.
- Benefits include faster data exploration, learning while working, error fixing, and maintaining data privacy.
- MLJAR Studio is positioned as a revolutionary tool for data science, integrating AI seamlessly into notebooks.
Show HN: Cogency – Cognitive Architecture for AI Agents
10 months ago
- Cogency is a multistep reasoning framework for building AI agents with minimal setup.
- Auto-detects providers (OpenAI, Anthropic, Gemini, Grok, Mistral) and intelligently routes tools.
- Streams transparent reasoning in real-time with plug-and-play tool integration.
- Features zero ceremony setup, intelligent tool routing, and beautiful execution traces.
- Supports Python 3.9+ and requires API keys in .env for auto-detection.
- Built-in tools include Calculator, Weather, Timezone, WebSearch, and FileManager.
- Uses Plan-Reason-Act-Reflect-Respond for multi-step reasoning.
- Designed for extension with custom LLM providers and tools.

first prev1next

About|Login

#ai agents

The Agentic Web and Original Sin

Mistral Agents API

Patched (YC S24) Is Hiring SWEs in Singapore

An agent is an LLM wrecking its environment in a loop

Show HN: A "Course" as an MCP Server

This AI Agent Should Have Been a SQL Query

From LLM to AI Agent: What's the Real Journey Behind AI System Development?

Show HN: Pickaxe – a TypeScript library for building AI agents

Introducing Qodo Gen CLI: Build and Run Coding Agents Anywhere in the SDLC

Reinforcement learning, explained with a minimum of math and jargon

Confidence in agentic AI: Why eval infrastructure must come first

What to build instead of AI agents

(Experiment) Colocating agent instructions with eng docs

Lessons to Get Started Building AI Agents

WASM Agents: AI agents running in the browser

Why the simplest desktop agent abstraction wins

From AI to Agents to Agencies

The MCP hype is a distraction. AI Agents should just build their own tools

AI Agents take control of our code

Show HN: Cogency – Cognitive Architecture for AI Agents