Carlos PEREZ @cepegma

I’m asking de myself if releasing 1000 feature a day is a good thing to do even if the aren’t technical constrains anymore. At the end of the day, there will be always a bottleneck somewhere either inside or outside the team and company.

Andrew Ng @AndrewYNg

2 months ago

AI-native software engineering teams operate very differently than traditional teams. The obvious difference is that AI-native teams use coding agents to build products much faster, but this leads to many other changes in how we operate. For example, some great engineers now play

229 390 2K 364K 2K

A more promising path to #AIG

How To Prompt @HowToPrompt__

2 months ago

Yann LeCun was right the entire time. And generative AI might be a dead end. For the last three years, the entire industry has been obsessed with building bigger LLMs. Trillions of parameters. Billions in compute. The theory was simple: if you make the model big enough, it will

434 2K 12K 1.3M 12K

I’m with @AndrewYNg when he recalls the scary future about everyone will be jobless comes from guys trying to sell their AI 🤖 stuff

Andrew Ng @AndrewYNg

3 months ago

As AI agents accelerate coding, what is the future of software engineering? Some trends are clear, such as the Product Management Bottleneck, referring to the idea that we are more constrained by deciding what to build rather than the actual building. But many implications, like

149 163 890 116K 506

As AI agents accelerate coding, what is the future of software engineering? Some trends are clear, such as the Product Management Bottleneck, referring to the idea that we are more constrained by deciding what to build rather than the actual building. But many implications, like AI’s impact on the job market, how software teams will be organized, and more, are still being sorted out. The theme of our AI Developer Conference on April 28-29 in San Francisco is The Future of Software Engineering. I look forward to speaking about this topic there, hearing from other speakers on this theme, and chatting with attendees about it. We’re shaping the future, and I hope you will join me there! It is currently trendy in some technology and policy circles to forecast massive job losses due to AI. Even if they have not yet materialized, these losses certainly must be just over the horizon! I have a contrarian view that the AI jobpocalypse — the notion that AI will lead to massive unemployment, perhaps even rioting in the streets — won’t be nearly as bad as dire forecasts by pundits, especially pundits who are trying to paint a picture of how powerful their AI technology is. Among professions, AI is accelerating software engineering most, given the rise of coding agents. According to a new report by Citadel Research, software engineering job postings are rising rapidly. So if software engineering is a harbinger of the impact AI will have on other professions, this expansion of software engineering jobs is encouraging. Yes, fresh college graduates are having a hard time finding jobs. And yes, there have been layoffs that CEOs have attributed to AI, even if a large fraction of this was “AI washing,” where businesses choose to attribute layoffs to AI, even though AI has not changed their internal operations much yet. And yes, there is a subset of job roles, such as call center operator, that are more heavily impacted. Many people are feeling significant job insecurity, and I feel for everyone struggling with employment, whether or not the cause is AI-related. And many other factors, such as over-hiring during the pandemic and high interest rates, have contributed to the slowdown in the labor market, and the notion that AI is leading to unemployment is oversimplified. In software engineering, I see a lot of exciting work ahead to adapt our workflows. It is already clear that: (i) As AI makes coding easier, a lot more people will be doing it. (ii) Writing code by hand and even reading (generated) code is not that important, because we can ask an LLM about the code and operate at a higher level than the raw syntax (although how high we can or should go is rapidly changing). (iii) There will be a lot more custom applications, because now it’s economical to write software for smaller and smaller audiences. (iv) Deciding what to build, more than the actual building, is becoming a bottleneck. (v) The cost of paying down technical debt is decreasing (since AI can refactor for you). At the same time, there are also a lot of open questions for our profession, such as: - In the future, what will be the key skills of a senior software engineer? And for junior levels, what should be the new Computer Science curriculum? - If everyone can build features, what skills, strategies, or resources create competitive advantage for individuals and for businesses? - What are the new building blocks (libraries, SDKs, etc.) of software? How do we organize coding agents to create software? - What should a software team look like? For example, how many engineers, product managers, designers, and so on. What tooling do we need to manage their workflow? - How do AI agents change the workflow of machine learning engineers and data scientists? For example, how can we use agents to accelerate exploring data, identifying hypotheses, and testing them? I’m excited to explore these and other questions about the future of software engineering at AI Dev. I expect this to be an exciting event. Please join us! [Original text: The Batch newsletter.] ai-dev.deeplearning.ai

Hope this was useful! I wanted to keep going but had to stop myself. Will post more soon. What are your favorite underrated Claude Code features?

no 👏 more 👏 permission prompts 👏

Claude @claudeai

3 months ago

New in Claude Code: auto mode. Instead of approving every file write and bash command, or skipping permissions entirely, auto mode lets Claude make permission decisions on your behalf. Safeguards check each action before it runs.

2K 3K 39K 7.8M 13K

Introducing TurboQuant: Our new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency. Read the blog to learn how it achieves these results: goo.gle/4bsq2qI

13/ A final tip: probably the most important thing to get great results out of Claude Code -- give Claude a way to verify its work. If Claude has that feedback loop, it will 2-3x the quality of the final result. Claude tests every single change I land to claude.ai/code using the Claude Chrome extension. It opens a browser, tests the UI, and iterates until the code works and the UX feels good. Verification looks different for each domain. It might be as simple as running a bash command, or running a test suite, or testing the app in a browser or phone simulator. Make sure to invest in making this rock-solid. code.claude.com/docs/en/chrome

Google Search algorithm leaked today. It outlines 2,596 modules with 14,014 ranking features related to various Google services. Here's what (13 things) we found:

The Godfather of AI is at #VivaTech! Yann LeCun (@ylecun) advises students coming into the industry: "Don't work on LLM. This is in the hands of large companies, there's nothing you can bring to the table. You should work on next-gen AI systems that lift the limitations of LLMs.

Exploring LLM Pricing With so many new LLMs, how do API costs compare? I delved into cost comparisons of models that I would use in production. Main takeaways: • cohere leads with cost-effective model • gpt-3.5 remains excellent value • mistral cost higher than anticipated • gemini 1.0 pro is pleasant surprise • gpt-4 is very expensive Models are ranked by input cost, asc.

Great example of data storytelling. Simply great!! ❤️

DataBeers Brussels @DataBeersBru

2 years ago

Kicking off 29th #databeers #brussels with our intro ! Here we talk about dogs 🐶 and cats 🐱 !

0 0 1 130 0

Our third speaker at #databeers #brussels is Rik Pauwels, showing how you can increase the efficiency of your big data usage ⚙️📈

Google's new flagship AI model, "Gemini," is set to be a direct competitor to GPT-4 and boasts computing power 5 times that of GPT-4. Trained on Google's TPUv5 chips, it's capable of simultaneous operations with a massive 16,384 chips. The dataset used for training this model is around 65 trillion tokens, and it's multi-modal, accepting text, video, audio, and pictures. Moreover, it can produce both text and images. The training also included content from YouTube and used advanced training techniques similar to "AlphaGo-type" methods. Google plans to release the Gemini model to the public in December 2023.

I want a laptop with a tensor processor and a unified memory system that runs Linux. Basically, something that can run large models without weighing a ton, requiring a separate GPU RAM, draining the battery in minutes, costing a fortune, and running a proprietary OS.

Well, #AI code writing assistants can make a good job on producing software. We need to be aware of their limitations and don’t blindly accept as the truth way they suggest #LLM #chatGPT lnkd.in/eW7bJwgm

Voici pourquoi il faut utiliser des #motsdepasse longs et complexes Découvre la vidéo de Ab développement ! #TikTok vm.tiktok.com/ZGJHCNu4w/

I hope you've enjoyed reading this thread. Please like/retweet the first tweet so others can as well. Follow me @lukesophinos for more.

Luke Sophinos @lukesophinos

3 years ago

In 2003, this guy created a dating site as a side-project. In 2015, he sold it for $575M. He kept all the money because he never raised a single cent. The sickest bootstrap story you've never heard of 🧵

292 4K 27K 7.5M 9K

The #AI porn business, when real human players will have a big plus compared to the new normal: lnkd.in/eYJ7B6fj

These graphics summarize some of life's hardest lessons. 1/ Distractions fill the space we give them.