D-i-L-i-a-N-a

AI strategist who makes complex systems simple and commercially relevant. All opinions are my own.

[Andrew Ng @AndrewYNg]

Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.

Frontend development — say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!

Backend development — say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.

Infrastructure. Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.

Research. Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.

Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.

I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future posts.

[Original text: https://t.co/rnnVWqebVe ]

[Andrew Ng @AndrewYNg]

How we prompt AI is very different in 2026 than 2022 when ChatGPT came out.

I'm teaching a new course, AI Prompting for Everyone, to help you become an AI power user — whatever your current skill level.

It covers skills that apply across ChatGPT, Gemini, Claude, and other AI tools. How to use deep research mode for well-researched reports on complex questions. How to give AI the right context, including more documents and images than most people realize you can provide. When to ask AI to think hard for several minutes on important decisions like what car to buy, what to study, or what job to take. And how to use AI to generate images, analyze data, and build simple games and websites.

I also cover intuitions about how these models work under the hood, so you know when to trust an answer and when not to.

Along the way, you'll see flying squirrels, a creativity test, some of my old family photos, and fireworks.

Join me at https://t.co/tcQc4iJAJG

[DavidLinthicum @DavidLinthicum]

The good, the bad, and the ugly of a decade of cloud computing: Security and agility are definite wins, but cost and complexity are serious drawbacks.

3 wins and 3 losses for cloud computing https://t.co/ItB6gDXBs7

[Mario @PawlowskiMario]

In #5G network (with high speed data transfer), the camera system with sim card in the vehicle in front; it becomes possible to see the front of that vehicle from behind

#IoT #Tech @jblefevre60 @Nicochan33 @JeroenBartelse @FrRonconi @mvollmer1 @rwang0

[D-i-L-i-a-N-a @mitevaUK]

Data Catalogs Are Dead; Long Live Data Discovery by Barr Moses in @TDataScience https://t.co/dUviPodvSe #DataScience #datamanagement #automation #BigData

[D-i-L-i-a-N-a @mitevaUK]

Top 10 Data and Analytics Trends for 2021 - InformationWeek https://t.co/FAtTiGUEfT via @InformationWeek #artificalintelligence #futuretech #AI

[asgard-data @AsgardData]

"If your company prides itself on making data-driven decisions, it’s important to recognize that those decisions will only ever be as good as your data."

https://t.co/TMIDELLiug

#DataDriven #DataQuality #DataAnalytics #BadData #AI

[D-i-L-i-a-N-a @mitevaUK]

AI-Based Drone Floated to Enforce Social Distancing https://t.co/m1GnQPnGZs via @datanami #AI #artificalintelligence

[D-i-L-i-a-N-a @mitevaUK]

Alphabet's DeepMind achieves historic new milestone in AI-based protein structure prediction https://t.co/Cgcki7eji3 via @techcrunch #ArtificialIntelligence #AI

[D-i-L-i-a-N-a @mitevaUK]

The Five Zones Every Data Lake Should Consider - NEOS https://t.co/t8PHYYWA6i #BigData #Analytics

[D-i-L-i-a-N-a @mitevaUK]

The Rules of Brainstorming Change When Artificial Intelligence Gets Involved. Here’s How. via @IDEO https://t.co/bHAGPtHlSD #ArtificialIntelligence #AI

[SS Supply Chain Solutions (3SC) @3SCIndia]

Are you listening to your data?
Big data analytics allows you to analyze growing volumes of structured and unstructured data that are often left untapped by conventional BI and analytics programs.

To know more, click on the below link:-
https://t.co/ryYYlN3QPC

#creatingvalue

[D-i-L-i-a-N-a @mitevaUK]

Read Gartner’s top 10 #data and #analytics tech trends for 2020 here.
https://t.co/JOMmGuj4yE
#ArtificialIntelligence #DataScience

[D-i-L-i-a-N-a @mitevaUK]

Your Cloud Migration is Actually an Agility Initiative by @datakitchen_io https://t.co/In4CtRPWtd
#Cloud #DataAnalytics #Analytics

[Savannah Thais 👩🏼‍💻 @BasicScienceSav]

important to remember! https://t.co/5t5QDgLsuu

[D-i-L-i-a-N-a @mitevaUK]

The economy of connecting https://t.co/PoXZvIC1wA via @HealthITNews #datamanagement #DigitalTransformation

[David Talby @davidtalby]

#ArtificialIntelligence in the Enterprise
https://t.co/NLFpZ20aNk
#ai #ml #dl #datascience #machinelearning #deeplearning #nlproc #nlp #textanalytics #textmining

[Bernard Marr @BernardMarr]

#Microsoft and #Shell have just announced a #partnership to work together to use #technology and #artificial #intelligence to help them address their #emissions challenges, reduce #CO2, and transition towards the use of #renewable #energy.

https://t.co/CRzxahLUxb

[D-i-L-i-a-N-a @mitevaUK]

Is Automation of Data Science Tasks the Future? https://t.co/39kEFI2Zrr via @https://twitter.com/analyticsinme #DataScience #MachineLearning #automation #Analytics #DataAnalytics

[Dr. Craig Brown @DrDataScientist]

Around the world, #weather sensors on satellites, on the ground, and in the oceans are providing a fire hose of weather and climate data. Find out how #AI is changing forecasting. #HPC @NOAA @NCAR_Science From @sgaudin Via @Enterprisenxt https://t.co/hkE7BKpf06