Загрузка видео...

Не удалось загрузить видео

На главную

Introducing Adaline 2.0 - The Agent Self-Improvement Layer Adaline turns Traces into Behaviors, Behaviors surface Issues, Issues become auto-generated Evals + Data, Adaline then generates new agent candidates and tests them. You review the winners and ship!

arshdilbagi's profile picture

Arsh Shah Dilbagi

2,582 subscribers

1,174,492 просмотров • 2 месяцев назад •via X (Twitter)

Наука и технологии
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Today, we're announcing content suggestions in Chatbase. Your agent can now self-improve by analyzing unresolved issues from past conversations, reviewing training data to surface conflicting information, then suggest improvements to fix any issues. Agents always fail in production unless they can learn from what went wrong. Missed answers, unresolved threads, and inconsistent knowledge. This is the missing layer that makes agents production-ready: continuous self-improvement.
0:56

Today, we're announcing content suggestions in Chatbase. Your agent can now self-improve by analyzing unresolved issues from past conversations, reviewing training data to surface conflicting information, then suggest improvements to fix any issues. Agents always fail in production unless they can learn from what went wrong. Missed answers, unresolved threads, and inconsistent knowledge. This is the missing layer that makes agents production-ready: continuous self-improvement.

Yasser

13,607 просмотров • 7 месяцев назад

The creator of Pydantic explained how to make live agents smarter after they ship in 1 hour 21 minutes - better than $2500 agent evals bootcamps. collect traces from your live agent -> score them with an eval -> run GEPA to auto-optimize the prompt -> deploy the winner -> repeat forever. That loop is how Pydantic AI agents self-improve while running in production. Pydantic AI + Logfire tracing + GEPA prompt optimizer + evals + feedback loops - that's the stack. Watch and save it, then wire GEPA into your live agent this week.
1:20:24

The creator of Pydantic explained how to make live agents smarter after they ship in 1 hour 21 minutes - better than $2500 agent evals bootcamps. collect traces from your live agent -> score them with an eval -> run GEPA to auto-optimize the prompt -> deploy the winner -> repeat forever. That loop is how Pydantic AI agents self-improve while running in production. Pydantic AI + Logfire tracing + GEPA prompt optimizer + evals + feedback loops - that's the stack. Watch and save it, then wire GEPA into your live agent this week.

h100envy

49,768 просмотров • 1 месяц назад

Introducing CodeRabbit Issue Planner! ✨ AI agents made coding fast but planning messy. Turn planning into a shared artifact in your issue tracker, grounded in related issues and decisions. Review prompts as a team, then hand them off to an agent!
1:30

Introducing CodeRabbit Issue Planner! ✨ AI agents made coding fast but planning messy. Turn planning into a shared artifact in your issue tracker, grounded in related issues and decisions. Review prompts as a team, then hand them off to an agent!

CodeRabbit

93,428 просмотров • 6 месяцев назад

Introducing Agent 2.0 Go from a simple prompt to fully realized marketing briefs and campaign assets right inside of Runway Agent. You can also easily analyze your performance data to improve your creative and scale it across platforms, formats and markets. Agent is being built to become the most capable autonomous agent for real-world work.
1:01

Introducing Agent 2.0 Go from a simple prompt to fully realized marketing briefs and campaign assets right inside of Runway Agent. You can also easily analyze your performance data to improve your creative and scale it across platforms, formats and markets. Agent is being built to become the most capable autonomous agent for real-world work.

Runway

954,084 просмотров • 2 месяцев назад

You probably have thousands of agent traces sitting in production. And most of them are doing nothing for you. Today we’re launching the new Kitaru to change that. Kitaru ingests traces from Langfuse, Braintrust, or any OTel source, investigates what went right and wrong, groups recurring failures into cohorts, and builds evaluators around them. Then you can replay those same production cases against a different model, prompt, context, or agent setup and ask: What would have happened if we changed this? Instead of traces being something you inspect after a failure, they become part of a continuous improvement loop. Open source. Free to use. Try it out today:
1:35

You probably have thousands of agent traces sitting in production. And most of them are doing nothing for you. Today we’re launching the new Kitaru to change that. Kitaru ingests traces from Langfuse, Braintrust, or any OTel source, investigates what went right and wrong, groups recurring failures into cohorts, and builds evaluators around them. Then you can replay those same production cases against a different model, prompt, context, or agent setup and ask: What would have happened if we changed this? Instead of traces being something you inspect after a failure, they become part of a continuous improvement loop. Open source. Free to use. Try it out today:

Hamza Tahir

22,264 просмотров • 7 дней назад

We're introducing Codex Security. An application security agent that helps you secure your codebase by finding vulnerabilities, validating them, and proposing fixes you can review and patch. Now, teams can focus on the vulnerabilities that matter and ship code faster.
0:40

We're introducing Codex Security. An application security agent that helps you secure your codebase by finding vulnerabilities, validating them, and proposing fixes you can review and patch. Now, teams can focus on the vulnerabilities that matter and ship code faster.

OpenAI Developers

1,718,072 просмотров • 5 месяцев назад

Introducing Capy, the world’s first multiplayer cloud coding platform. Capy plans, builds, tests, and reviews your code to ship self-healing PRs fully async. PR view, review agent, computer use, Slack, Linear, ChatGPT sub, all in one tool. Comment to get $100 of free credits.
1:25

Introducing Capy, the world’s first multiplayer cloud coding platform. Capy plans, builds, tests, and reviews your code to ship self-healing PRs fully async. PR view, review agent, computer use, Slack, Linear, ChatGPT sub, all in one tool. Comment to get $100 of free credits.

Capy

133,396 просмотров • 4 месяцев назад

Introducing Claude Engineer 2.0, with agents! 🚀 Biggest update yet with the addition of a code editor and code execution agents, and dynamic editing. When editing files (especially large ones), Engineer will direct a coding agent, and the agent will provide changes in batches. Batches are smartly selected based on file complexity. The code execution agent will run the code and check for issues. It can even start processes (like live servers) and end them. It's insanely powerful! 🔥
2:25

Introducing Claude Engineer 2.0, with agents! 🚀 Biggest update yet with the addition of a code editor and code execution agents, and dynamic editing. When editing files (especially large ones), Engineer will direct a coding agent, and the agent will provide changes in batches. Batches are smartly selected based on file complexity. The code execution agent will run the code and check for issues. It can even start processes (like live servers) and end them. It's insanely powerful! 🔥

Pietro Schirano

393,686 просмотров • 2 лет назад

Introducing Decipher AI: agentic QA for the era of coding agents. Show it your product, and an agent generates E2E tests in minutes, maintains them for you, and catches bugs before customers do so. You keep shipping fast. Plus, Decipher will detect when live customers hit bugs and alert you.
0:59

Introducing Decipher AI: agentic QA for the era of coding agents. Show it your product, and an agent generates E2E tests in minutes, maintains them for you, and catches bugs before customers do so. You keep shipping fast. Plus, Decipher will detect when live customers hit bugs and alert you.

Michael Rosenfield

12,255 просмотров • 6 месяцев назад

The new GitHub Copilot coding agent can work in the background while you focus on other tasks. Assign it tasks via GitHub Issues or ad hoc in Visual Studio Code. Watch this demo and then optimize your workflow with the Copilot coding agent. 🚀 ⬇️
2:03

The new GitHub Copilot coding agent can work in the background while you focus on other tasks. Assign it tasks via GitHub Issues or ad hoc in Visual Studio Code. Watch this demo and then optimize your workflow with the Copilot coding agent. 🚀 ⬇️

GitHub

55,411 просмотров • 1 год назад

Today we're introducing Wave. A proactive product agent that helps teams build self-improving products. Every product team runs the same loop. Build, ship, use, learn. AI has made building and shipping extraordinarily fast. Understanding usage and learning still happen by hand. Wave runs the whole loop: - Analyzes your Amplitude data for signals across analytics, feedback, session replays, error logs, agent traces, and experiment results - Surfaces opportunities in the form of full product specs that you or your agents can approve and ship - Tracks the experiments and outcomes of what you've built so you can start the loop over again Your product learns what works and gets smarter every cycle:
2:36

Today we're introducing Wave. A proactive product agent that helps teams build self-improving products. Every product team runs the same loop. Build, ship, use, learn. AI has made building and shipping extraordinarily fast. Understanding usage and learning still happen by hand. Wave runs the whole loop: - Analyzes your Amplitude data for signals across analytics, feedback, session replays, error logs, agent traces, and experiment results - Surfaces opportunities in the form of full product specs that you or your agents can approve and ship - Tracks the experiments and outcomes of what you've built so you can start the loop over again Your product learns what works and gets smarter every cycle:

Spenser Skates

114,319 просмотров • 2 месяцев назад

Automatically create Agent Skills from past sessions. Gemini CLI can now comb through past session data and suggest new skills based on past patterns of things you do frequently! Helps the agent self-improve. 🧠 Enable Auto Memory in /settings to try it out.
0:20

Automatically create Agent Skills from past sessions. Gemini CLI can now comb through past session data and suggest new skills based on past patterns of things you do frequently! Helps the agent self-improve. 🧠 Enable Auto Memory in /settings to try it out.

Jack Wotherspoon

24,793 просмотров • 3 месяцев назад

Systematic literature reviews take 12-18 months to complete. Looks like AI is going to fully automate systematic reviews sooner than later. SciSpace ( SciSpace) just launched an autonomous AI agent that conducts a systematic literature review with a single prompt. Go to scispace[.]com and run the following prompt: "Conduct a systematic literature review on [your topic]" SciSpace agent will generate research questions based on the PICO framework. You can review these questions and edit them according to your specific requirements. The agent will also draft screening criteria that you can edit according to your needs. Then the agent asks you to select the databases you want to use and the date range for paper. After this step, everything is fully automated. The agent will search for papers in the relevant databases, it will combine and rerank the papers. Then it will start the title and abstract screening and include the papers that meet the include criteria. In the next step, it will download the full text of included papers and screen them followed by data extraction. Based on the extracted data, it generates a complete systematic literature review and also a PRISMA diagram. It will also give you a table of papers included along with the rational for including them. The only thing that is keeping AI agents to fully automate systematic literature reviews fields is the papers behind paywalls. Check out the agent at scispace[.]com and see if you find its review useful.
0:47

Systematic literature reviews take 12-18 months to complete. Looks like AI is going to fully automate systematic reviews sooner than later. SciSpace ( SciSpace) just launched an autonomous AI agent that conducts a systematic literature review with a single prompt. Go to scispace[.]com and run the following prompt: "Conduct a systematic literature review on [your topic]" SciSpace agent will generate research questions based on the PICO framework. You can review these questions and edit them according to your specific requirements. The agent will also draft screening criteria that you can edit according to your needs. Then the agent asks you to select the databases you want to use and the date range for paper. After this step, everything is fully automated. The agent will search for papers in the relevant databases, it will combine and rerank the papers. Then it will start the title and abstract screening and include the papers that meet the include criteria. In the next step, it will download the full text of included papers and screen them followed by data extraction. Based on the extracted data, it generates a complete systematic literature review and also a PRISMA diagram. It will also give you a table of papers included along with the rational for including them. The only thing that is keeping AI agents to fully automate systematic literature reviews fields is the papers behind paywalls. Check out the agent at scispace[.]com and see if you find its review useful.

Mushtaq Bilal, PhD

42,003 просмотров • 4 месяцев назад

AI agents with full tool use is going to be quite insane. Here's Claude using the Box and Linear MCP servers to take product roadmap docs from Box and turns them into issues to track. This is a small example of what the future of AI agent interoperability looks like.
1:17

AI agents with full tool use is going to be quite insane. Here's Claude using the Box and Linear MCP servers to take product roadmap docs from Box and turns them into issues to track. This is a small example of what the future of AI agent interoperability looks like.

Aaron Levie

87,943 просмотров • 1 год назад

A conversational agent with no hallucinations! This is huge! Here is the $1M problem: Build a customer-facing AI agent that works consistently, doesn't hallucinate, and is fully aligned with the business rules. Check out Parlant, a FREE, open-source library. I've talked about them before, and here is the TL;DR: 1. Parlant gives you 10x more control over what your agent says 2. It helps you control how agents conduct conversations 3. It helps you control the service protocols and rules agents follow 4. It helps you control what agents can and can't say They do something pretty clever: 1. They have a set of predefined potential responses that the agent can use to reply to the customer. 2. When a customer sends a message, the agent generates a fluid response draft using an LLM and matches and renders the closest pre-approved reply template. 3. The agent returns the rendered pre-approved reply, not the one generated by the LLM. This maintains fluidity while avoiding unpredictability. Parlant also uses your domain terminology, invokes your tools, and implements self-critique to craft/evaluate its response iteratively. This pretty much solves any hallucination issues with your agent.
0:45

A conversational agent with no hallucinations! This is huge! Here is the $1M problem: Build a customer-facing AI agent that works consistently, doesn't hallucinate, and is fully aligned with the business rules. Check out Parlant, a FREE, open-source library. I've talked about them before, and here is the TL;DR: 1. Parlant gives you 10x more control over what your agent says 2. It helps you control how agents conduct conversations 3. It helps you control the service protocols and rules agents follow 4. It helps you control what agents can and can't say They do something pretty clever: 1. They have a set of predefined potential responses that the agent can use to reply to the customer. 2. When a customer sends a message, the agent generates a fluid response draft using an LLM and matches and renders the closest pre-approved reply template. 3. The agent returns the rendered pre-approved reply, not the one generated by the LLM. This maintains fluidity while avoiding unpredictability. Parlant also uses your domain terminology, invokes your tools, and implements self-critique to craft/evaluate its response iteratively. This pretty much solves any hallucination issues with your agent.

Santiago

64,237 просмотров • 1 год назад

Kicking off the Document Agent Olympics. Build a document agent - Win $200 🥇 Document agents turn messy PDFs, invoices, and filings into structured data you can actually use. Think: extracting financials from SEC filings, reconciling invoices against contracts, or processing a stack of resumes to surface top candidates. We're giving away three $200 prizes for the best agents built over the next 3 weeks. To enter: 💛 Deploy the agent to LlamaCloud 🤝 Make sure the agent repository is public 🚀 Explain what your document agent is solving 🔥 Bonus points for a good readme and demo video And most importantly, let us know what you think about our new LlamaAgents Builder! Ready to participate? Join the contest and start building your winning agent! Signup to LlamaCloud to get started
0:35

Kicking off the Document Agent Olympics. Build a document agent - Win $200 🥇 Document agents turn messy PDFs, invoices, and filings into structured data you can actually use. Think: extracting financials from SEC filings, reconciling invoices against contracts, or processing a stack of resumes to surface top candidates. We're giving away three $200 prizes for the best agents built over the next 3 weeks. To enter: 💛 Deploy the agent to LlamaCloud 🤝 Make sure the agent repository is public 🚀 Explain what your document agent is solving 🔥 Bonus points for a good readme and demo video And most importantly, let us know what you think about our new LlamaAgents Builder! Ready to participate? Join the contest and start building your winning agent! Signup to LlamaCloud to get started

LlamaIndex 🦙

12,111 просмотров • 6 месяцев назад

Sharing an OS tool I have been building for a few weeks: - git for agent traces. With OT, you have a local client that helps you santise, review and securely share traces from your coding sessions, privately or publicly, as a Hugging Face dataset. 0.3 adds experimental session blame which I am very excited about - attributing commit/PR changes back to the traces that produced them (all local) unlocks a new layer in observability and issue tracking. A few short demos of the local TUI client and the upcoming opentraces hub platform. Try it out! keen on your feedback🙏
0:35

Sharing an OS tool I have been building for a few weeks: - git for agent traces. With OT, you have a local client that helps you santise, review and securely share traces from your coding sessions, privately or publicly, as a Hugging Face dataset. 0.3 adds experimental session blame which I am very excited about - attributing commit/PR changes back to the traces that produced them (all local) unlocks a new layer in observability and issue tracking. A few short demos of the local TUI client and the upcoming opentraces hub platform. Try it out! keen on your feedback🙏

Gabriele Farei

10,565 просмотров • 4 месяцев назад

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.
0:31

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.

OptimAI Network

50,452 просмотров • 11 месяцев назад

Introducing Agent Wikis! I've been using llm wikis (or knowledge bases) for research and videos for a while to ensure the information I'm providing is always up-to-date and accurate. Now you can access them for free! Just point your agent at to access wikis on many topics, including Hermes Agent, Hyperframes, Google Ads, and llama.cpp. They can be queried through a simple MCP server. Everything has been designed to be agent-friendly and lightweight (no context bloat). This is a beta release, so please let me know of any bugs or issues (I'm sure there are one or two). You can also join the waitlist if you would like us to create and maintain your own private wiki, or if you are a project owner you can request one for your project to make sure your users (and their agents) don't run into any common issues (and also be featured on the site). Thank you!
0:30

Introducing Agent Wikis! I've been using llm wikis (or knowledge bases) for research and videos for a while to ensure the information I'm providing is always up-to-date and accurate. Now you can access them for free! Just point your agent at to access wikis on many topics, including Hermes Agent, Hyperframes, Google Ads, and llama.cpp. They can be queried through a simple MCP server. Everything has been designed to be agent-friendly and lightweight (no context bloat). This is a beta release, so please let me know of any bugs or issues (I'm sure there are one or two). You can also join the waitlist if you would like us to create and maintain your own private wiki, or if you are a project owner you can request one for your project to make sure your users (and their agents) don't run into any common issues (and also be featured on the site). Thank you!

tonbi

46,993 просмотров • 2 месяцев назад

New session with Shreya Shankar on How to Automate Evals with AI (correctly) The most important part of the eval workflow is finding issues. Shreya does a live demo of how to steer the AI iteratively to find unknown unknowns Chapter summaries: 00:00:00 Introduction 00:00:55 Why vendors want to automate evals for you 00:01:45 Why no tool can fully automate evals 00:06:00 Mistake #1: Asking AI to just "find the issues" in your data 00:08:11 What good AI-assisted error analysis actually looks like 00:11:12 Live demo: building a review interface from scratch 00:15:10 Annotating traces and building a failure mode taxonomy 00:19:31 Mistake #2: Only reviewing your data once 00:22:53 Mistake #3: Treating all apps with the same accuracy bar 00:25:10 General-purpose agents vs. dedicated eval tools 00:25:48 Summary: three rules for automating evals correctly YT links and more in reply
27:18

New session with Shreya Shankar on How to Automate Evals with AI (correctly) The most important part of the eval workflow is finding issues. Shreya does a live demo of how to steer the AI iteratively to find unknown unknowns Chapter summaries: 00:00:00 Introduction 00:00:55 Why vendors want to automate evals for you 00:01:45 Why no tool can fully automate evals 00:06:00 Mistake #1: Asking AI to just "find the issues" in your data 00:08:11 What good AI-assisted error analysis actually looks like 00:11:12 Live demo: building a review interface from scratch 00:15:10 Annotating traces and building a failure mode taxonomy 00:19:31 Mistake #2: Only reviewing your data once 00:22:53 Mistake #3: Treating all apps with the same accuracy bar 00:25:10 General-purpose agents vs. dedicated eval tools 00:25:48 Summary: three rules for automating evals correctly YT links and more in reply

Hamel Husain

34,196 просмотров • 1 месяц назад