Anowar

Wanderer!!!

[Anowar @arkentos]

Finished my max weekly quota, time to go to bed!

[Anowar @arkentos]

Going through PR commits this morning. We need an "intelligent diff" tool. Not just normal "git diff", shows me which changes are core logic vs noise, what the semantic impact is, what downstream code this touches. Harness engineering could use this.

[Anowar @arkentos]

@feross @amasad Weirdly thinking freedom is not free, you gotta protect it. More power to Socket and similar tools. Things will be wilder and we will need tools like these or people will stop trusting opensource.

[Anowar @arkentos]

@elonmusk so much love for these cars!

[Anowar @arkentos]

I now am getting no claude, codex or copilot time at home

[Anowar @arkentos]

The moment you realize you do not need to see youtube those information sparse tutorial-ish videos, no more. All hail the CLI revolution.

[Anowar @arkentos]

@karpathy Summarized so well, and copying your quote.
"You're building magic objects of the future, you rock! And I love my car"

[Anowar @arkentos]

@karpathy lol this is so funny and informative at the same time

[Anowar @arkentos]

@claudeai well the recently the reliability and availability has definitely take a hit, what can I do?

[Anowar @arkentos]

@AnthropicAI I love AI spring time!!!

[Anowar @arkentos]

@demishassabis @lmthang That is so amazing. Chem and Physics on the horozon?

[Demis Hassabis @demishassabis]

Official results are in - Gemini achieved gold-medal level in the International Mathematical Olympiad! 🏆 An advanced version was able to solve 5 out of 6 problems. Incredible progress - huge congrats to @lmthang and the team! https://t.co/pp9bXF7rVj

[Anowar @arkentos]

Claude-ing, Codex-ing. Such a prosperity enabler path!!!

[Elon Musk @elonmusk]

Grinding on @Grok all night with the @xAI team. Good progress.

Will be called Grok 4. Release just after July 4th. Needs one more big run for a specialized coding model.

[Sundar Pichai @sundarpichai]

Gemini 2.5 Pro + 2.5 Flash are now stable and generally available. Plus, get a preview of Gemini 2.5 Flash-Lite, our fastest + most cost-efficient 2.5 model yet. 🔦

Exciting steps as we expand our 2.5 series of hybrid reasoning models that deliver amazing performance at the Pareto frontier of cost and speed. 🚀

[Anowar @arkentos]

This possibly is how SWE will evolve

[Andrew Ng @AndrewYNg]

There’s a new breed of GenAI Application Engineers who can build more-powerful applications faster than was possible before, thanks to generative AI. Individuals who can play this role are highly sought-after by businesses, but the job description is still coming into focus. Let me describe their key skills, as well as the sorts of interview questions I use to identify them.

Skilled GenAI Application Engineers meet two primary criteria: (i) They are able to use the new AI building blocks to quickly build powerful applications. (ii) They are able to use AI assistance to carry out rapid engineering, building software systems in dramatically less time than was possible before. In addition, good product/design instincts are a significant bonus.

AI building blocks. If you own a lot of copies of only a single type of Lego brick, you might be able to build some basic structures. But if you own many types of bricks, you can combine them rapidly to form complex, functional structures. Software frameworks, SDKs, and other such tools are like that. If all you know is how to call a large language model (LLM) API, that's a great start. But if you have a broad range of building block types — such as prompting techniques, agentic frameworks, evals, guardrails, RAG, voice stack, async programming, data extraction, embeddings/vectorDBs, model fine tuning, graphDB usage with LLMs, agentic browser/computer use, MCP, reasoning models, and so on — then you can create much richer combinations of building blocks.

The number of powerful AI building blocks continues to grow rapidly. But as open-source contributors and businesses make more building blocks available, staying on top of what is available helps you keep on expanding what you can build. Even though new building blocks are created, many building blocks from 1 to 2 years ago (such as eval techniques or frameworks for using vectorDBs) are still very relevant today.

AI-assisted coding. AI-assisted coding tools enable developers to be far more productive, and such tools are advancing rapidly. Github Copilot, first announced in 2021 (and made widely available in 2022), pioneered modern code autocompletion. But shortly after, a new breed of AI-enabled IDEs such as Cursor and Windsurf offered much better code-QA and code generation. As LLMs improved, these AI-assisted coding tools that were built on them improved as well.

Now we have highly agentic coding assistants such as OpenAI’s Codex and Anthropic’s Claude Code (which I really enjoy using and find impressive in its ability to write code, test, and debug autonomously for many iterations). In the hands of skilled engineers — who don’t just “vibe code” but deeply understand AI and software architecture fundamentals and can steer a system toward a thoughtfully selected product goal — these tools make it possible to build software with unmatched speed and efficiency.

I find that AI-assisted coding techniques become obsolete much faster than AI building blocks, and techniques from 1 or 2 years ago are far from today's best practices. Part of the reason for this might be that, while AI builders might use dozens (hundreds?) of different building blocks, they aren’t likely to use dozens of different coding assistance tools at once, and so the forces of Darwinian competition are stronger among tools. Given the massive investments in this space by Anthropic, Google, OpenAI, and other players, I expect the frenetic pace of development to continue, but keeping up with the latest developments in AI-assisted coding tools will pay off, since each generation is much better than the last.

Bonus: Product skills. In some companies, engineers are expected to take pixel-perfect drawings of a product, specified in great detail, and write code to implement it. But if a product manager has to specify even the smallest detail, this slows down the team. The shortage of AI product managers exacerbates this problem. I see teams move much faster if GenAI Engineers also have some user empathy as well at basic skill at designing products, so that, given only high-level guidance on what to build (“a user interface that lets users see their profiles and change their passwords”), they can make a lot of decisions themselves and build at least a prototype to iterate from.

When interviewing GenAI Application Engineers, I will usually ask about their mastery of AI building blocks and ability to use AI-assisted coding, and sometimes also their product/design instincts. One additional question I've found highly predictive of their skill is, “How do you keep up with the latest developments in AI?” Because AI is evolving so rapidly, someone with good strategies for keeping up — such as reading The Batch and taking short courses 😃, regular hands-on practice building projects, and having a community to talk to — really does stay ahead of the game.

[Original post: https://t.co/I3alxNs0vn ]

[Anowar @arkentos]

@OpenAI waiting!!!

[Anowar @arkentos]

From the age of coding to the age of clauding :)

[Anthropic @AnthropicAI]

Projects on Claude now support 10x more content.

When you add files beyond the existing threshold, Claude switches to a new retrieval mode to expand the functional context.