Simple Notes: Agentic search is (probably) the solution to all of your context problems and agent reliability issues. This talk by Cline's Ara Khan explains why they went from "evals are useless" to using them as a core part of my agent ...

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Agentic search is (probably) the solution to all of your context problems and agent reliability issues. This talk by Cline's Ara Khan explains why they went from "evals are useless" to using them as a core part of my agent ...

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  • This talk by Cline's Ara Khan explains why they went from "evals are useless" to using them as a core part of my agent ...
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Media Gallery

AI Dev 26 x SF | Andrew K.  Davies: Deterministic Memory: How to Build an AI That Cannot Lie
AI Dev 26 x SF | Andrew Filev: Multi Model Pipelines—How to Get Better AI Results for Less
AI Dev 26 x SF: Andrew Ng: The Future of Software Engineering
AI Dev 26 x SF | Matthew Xu: The 4-Legged Identity Challenge
AI Dev 26 x SF | Marc Brooker: It's Time to Be Right
AI Dev 26 x SF | Diamond Bishop: The Next 100 Agents. Building the Agent Native Office
AI Dev 26 x SF | Tom Howlett: Can LLMs Generate Enterprise Quality Code?
AI Dev 26 x SF | Jeff Huber: Everything You Need to Know About Agentic Search
AI Dev 26 x SF | Ara Khan: Evals Are Broken Use Them Anyway
AI Dev 26 x SF | Eda Zhou & Mahdi Ghodsi: Building Personal AI Agents with Open Source Models
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AI Dev 26 x SF | Andrew K.  Davies: Deterministic Memory: How to Build an AI That Cannot Lie

AI Dev 26 x SF | Andrew K. Davies: Deterministic Memory: How to Build an AI That Cannot Lie

Read more details and related context about AI Dev 26 x SF | Andrew K. Davies: Deterministic Memory: How to Build an AI That Cannot Lie.

AI Dev 26 x SF | Andrew Filev: Multi Model Pipelines—How to Get Better AI Results for Less

AI Dev 26 x SF | Andrew Filev: Multi Model Pipelines—How to Get Better AI Results for Less

Read more details and related context about AI Dev 26 x SF | Andrew Filev: Multi Model Pipelines—How to Get Better AI Results for Less.

AI Dev 26 x SF: Andrew Ng: The Future of Software Engineering

AI Dev 26 x SF: Andrew Ng: The Future of Software Engineering

Read more details and related context about AI Dev 26 x SF: Andrew Ng: The Future of Software Engineering.

AI Dev 26 x SF | Matthew Xu: The 4-Legged Identity Challenge

AI Dev 26 x SF | Matthew Xu: The 4-Legged Identity Challenge

As MCP systems scale from local setups to shared infrastructure,

AI Dev 26 x SF | Marc Brooker: It's Time to Be Right

AI Dev 26 x SF | Marc Brooker: It's Time to Be Right

Read more details and related context about AI Dev 26 x SF | Marc Brooker: It's Time to Be Right.

AI Dev 26 x SF | Diamond Bishop: The Next 100 Agents. Building the Agent Native Office

AI Dev 26 x SF | Diamond Bishop: The Next 100 Agents. Building the Agent Native Office

Read more details and related context about AI Dev 26 x SF | Diamond Bishop: The Next 100 Agents. Building the Agent Native Office.

AI Dev 26 x SF | Tom Howlett: Can LLMs Generate Enterprise Quality Code?

AI Dev 26 x SF | Tom Howlett: Can LLMs Generate Enterprise Quality Code?

Read more details and related context about AI Dev 26 x SF | Tom Howlett: Can LLMs Generate Enterprise Quality Code?.

AI Dev 26 x SF | Jeff Huber: Everything You Need to Know About Agentic Search

AI Dev 26 x SF | Jeff Huber: Everything You Need to Know About Agentic Search

Agentic search is (probably) the solution to all of your context problems and agent reliability issues. Jeff Huber from Chroma, ...

AI Dev 26 x SF | Ara Khan: Evals Are Broken Use Them Anyway

AI Dev 26 x SF | Ara Khan: Evals Are Broken Use Them Anyway

This talk by Cline's Ara Khan explains why they went from "evals are useless" to using them as a core part of my agent ...

AI Dev 26 x SF | Eda Zhou & Mahdi Ghodsi: Building Personal AI Agents with Open Source Models

AI Dev 26 x SF | Eda Zhou & Mahdi Ghodsi: Building Personal AI Agents with Open Source Models

Read more details and related context about AI Dev 26 x SF | Eda Zhou & Mahdi Ghodsi: Building Personal AI Agents with Open Source Models.