Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

AI didn't make code cheap. it made bad code lethal. Matt Pocock: "Software fundamentals matter more than ever" AI in a good codebase ships gold. AI in a bad codebase ships garbage. framework mastery doesn't compound. fundamentals do. if you're starting with AI agents, build these 5 patterns before...

28,480 Aufrufe • vor 5 Monaten •via X (Twitter)

20 Kommentare

Profilbild von Rohit
Rohitvor 5 Monaten

500 AI Agents project here:

Profilbild von Rohit
Rohitvor 5 Monaten

Full talk by @mattpocockuk on @aiDotEngineer here:

Profilbild von Nihit Singh
Nihit Singhvor 5 Monaten

Crzy

Profilbild von Siddharth
Siddharthvor 5 Monaten

fundamentals are gonna stay

Profilbild von Mohamed Anis
Mohamed Anisvor 5 Monaten

You've captured exactly why we're seeing such divergent AI outcomes. the quality of underlying architecture determines whether AI accelerates value or amplifies technical debt at scale.

Profilbild von Khairallah AL-Awady
Khairallah AL-Awadyvor 5 Monaten

banger my man

Profilbild von Alpha Batcher
Alpha Batchervor 5 Monaten

Without these 5 patterns, no work in AI will be successful

Profilbild von J A Z I I
J A Z I Ivor 5 Monaten

Love it man you cooking with all bangers

Profilbild von CyrilXBT
CyrilXBTvor 5 Monaten

banger right here

Profilbild von Crio Songo
Crio Songovor 5 Monaten

You’re 100% right about this. I’ve seen too many teams cut corners on fundamentals just because AI can crank out code fast. I actually just started practicing these 5 agent patterns last week, this post is exactly the reminder I needed.

Profilbild von 唐清乐
唐清乐vor 5 Monaten

这句话很准。AI放大的是系统本身的质量,好的工程基础会被加速,坏的抽象也会被更快复制。基础功反而更值钱了。

Profilbild von Morton Digital
Morton Digitalvor 5 Monaten

That 5th pattern — Building Trustworthy AI Agents — is where most builders get burned in production. Error cascades, retry storms, context loss when the LLM 429s. Put together a playbook on exactly those failure modes:

Profilbild von robertus
robertusvor 5 Monaten

bad code used to tax maintainers slowly. agents let it scale its revenge with much better throughput

Profilbild von Himanshu
Himanshuvor 5 Monaten

Interesting

Profilbild von Harry Tandy
Harry Tandyvor 5 Monaten

a solid architecture is the only thing standing between a working product and a total mess

Profilbild von Levi
Levivor 5 Monaten

the “orchestrator-subagent is the only shape that ships” take is spicy… what breaks first when you skip it

Profilbild von Dylan Swanepoel
Dylan Swanepoelvor 5 Monaten

AI amplified one thing more than anything else: Architecture debt. Bad foundations used to slow teams down. Now they poison the output of the agents operating on top of them. That’s why so many AI products demo well but collapse operationally.

Profilbild von robertus
robertusvor 5 Monaten

bad code used to tax maintainers slowly. agents let it scale its revenge with much better throughput

Profilbild von Carlos Tecnico
Carlos Tecnicovor 5 Monaten

drop F vibe coding. fundamentals or your agent ships trash 🙄

Profilbild von Avid
Avidvor 5 Monaten

good one

Ähnliche Videos

Announcing my new course: Agentic AI! Building AI agents is one of the most in-demand skills in the job market. This course, available now at teaches you how. You'll learn to implement four key agentic design patterns: - Reflection, in which an agent examines its own output and figures out how to improve it - Tool use, in which an LLM-driven application decides which functions to call to carry out web search, access calendars, send email, write code, etc. - Planning, where you'll use an LLM to decide how to break down a task into sub-tasks for execution, and - Multi-agent collaboration, in which you build multiple specialized agents — much like how a company might hire multiple employees — to perform a complex task You'll also learn to take a complex application and systematically decompose it into a sequence of tasks to implement using these design patterns. But here's what I think is the most important part of this course: Having worked with many teams on AI agents, I've found that the single biggest predictor of whether someone executes well is their ability to drive a disciplined process for evals and error analysis. In this course, you'll learn how to do this, so you can efficiently home in on which components to improve in a complex agentic workflow. Instead of guessing what to work on, you'll let evals data guide you. This will put you significantly ahead of the game compared to the vast majority of teams building agents. Together, we'll build a deep research agent that searches, synthesizes, and reports, using all of these agentic design patterns and best practices. This self-paced course is taught in a vendor neutral way, using raw Python - without hiding details in a framework. You'll see how each step works, and learn the core concepts that you can then implement using any popular agentic AI framework, or using no framework. The only prerequisite is familiarity with Python, though knowing a bit about LLMs helps. Come join me, and let's build some agentic AI systems! Sign up to get started:

Andrew Ng

891,810 Aufrufe • vor 1 Jahr