Andrea Trocino @andics

@Jason Instagram, Android and Youtube were nowhere close to $250bn though

Dogfooding Opus 4.7 the last few weeks, I've been feeling incredibly productive. Sharing a few tips to get more out of 4.7 🧵

@deedydas @grok compare to openAI and xAI revenues

I feel the same on any platform. The curation which I used to have 15y on google reader is gone, but it is the result of all the social incentives on content. I am starting to thing that the only solution is not to substitute those incentive with your own money to directly pay more serious sources

A few random notes from claude coding quite a bit last few weeks. Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent. IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits. Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased. Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion. Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage. Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building. Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it. Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements. Questions. A few of the questions on my mind: - What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*. - Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro). - What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music? - How much of society is bottlenecked by digital knowledge work? TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.

We’re releasing TranslateGemma, a new family of open translation models with support for 55 languages. 🌐 Available in 4B, 12B, and 27B parameter sizes – they’re designed for efficiency without sacrificing quality.

@EHuanglu Super cool, well done and thanks for sharing

1. i cant believe people with no code experience at all can build an advanced website like this with AI now you can chat(text) with AI agent to add 3D models, functions and adjust UI.. this is beyond incredible.. app preview: gesturemodel.emergent.host code&prompts at end

x.com/i/article/2002…

GPT-5.2 just overtook Claude Opus 4.5 to achieve the highest score in GDPval-AA, a benchmark that focuses on performance in real-world economically valuable tasks However, GPT-5.2 is also the most expensive model to run GDPval-AA: GPT-5.2 cost $620, compared to Claude Opus 4.5’s $608 and GPT-5.1’s $88. This was driven by @OpenAI's GPT-5.2 using >6x more tokens than GPT-5.1 (250M compared to 40M), and OpenAI raising prices by 40% ($14/$1.75 per million input/output tokens compared to $1.25/$10). GDPval-AA uses our agentic harness, Stirrup, to run models on OpenAI's GDPval dataset, and measures their performance using an AI-based grading pipeline. Full set of Artificial Analysis Intelligence Index benchmarks are in progress and we will be sharing a full update when complete.

The UK is an amazing place for science & innovation. Thrilled to deepen our partnership with the UK Government to turbocharge scientific discovery with AI - giving scientists here priority access to our frontier models like AlphaEvolve, AI Co-Scientist, AlphaGenome, WeatherNext & more. We’re also building our first automated lab here in the UK for materials science!

@AnthropicAI just donated the Model Context Protocol (MCP) to the new Agentic AI Foundation, a Linux Foundation project, alongside other open standards such as AGENTS.md and Goose. Curious what everyone thinks about this move — is it primarily to accelerate MCP as an open standard and broaden adoption, or a signal that Anthropic sees more long-term value in Claude Skills and expects MCP to become less central over time?

Quick new post: Auto-grading decade-old Hacker News discussions with hindsight I took all the 930 frontpage Hacker News article+discussion of December 2015 and asked the GPT 5.1 Thinking API to do an in-hindsight analysis to identify the most/least prescient comments. This took ~3 hours to vibe code and ~1 hour and $60 to run. The idea was sparked by the HN article yesterday where Gemini 3 was asked to hallucinate the HN front page one decade forward. More generally: 1. in-hindsight analysis has always fascinated me as a way to train your forward prediction model so reading the results is really interesting and 2. it's worth contemplating what it looks like when LLM megaminds of the future can do this kind of work a lot cheaper, faster and better. Every single bit of information you contribute to the internet can (and probably will be) scrutinized in great detail if it is "free". Hence also my earlier tweet from a while back - "be good, future LLMs are watching". Congrats to the top 10 accounts pcwalton, tptacek, paulmd, cstross, greglindahl, moxie, hannob, 0xcde4c3db, Manishearth, and johncolanduoni - GPT 5.1 Thinking found your comments to be the most insightful and prescient of all comments of HN in December of 2015. Links: - A lot more detail in my blog post karpathy.bearblog.dev/auto-grade-hn/ - GitHub repo of the project if you'd like to play github.com/karpathy/hn-ti… - The actual results pages for your reading pleasure karpathy.ai/hncapsule/

Today, Netflix announced our acquisition of Warner Bros. Together, we’ll define the next century of storytelling, creating an extraordinary entertainment offering for audiences everywhere. about.netflix.com/en/news/netfli…

🚨GOOGLE ANTIGRAVITY HAS FREE OPUS 4.5 THINKING🚨 Hey guys, you are welcome 😜. Antigravity as a package is insane right now with the inclusion of Opus 4.5. Is OpenAI dead? We will see next week I guess, but they are so so behind😭 Also, I tried GPT-5.1-Codex-Max vs. Opus 4.5 for the same task, and Opus 4.5 was way ahead of it. So, OpenAI better come up with something very soon. Because there is no reason to use any of their products right now. Thank you @bored_ya for informing me about this change 🙏

@emollick Amazing, copied & improved it with the right food

@NanoBanana Looks very cool, and such a coincidence that the central is delayed

@deepseek_ai Really appreciate the open source releases.

@ArtificialAnlys Great resource, added to my bookmarks

@deepseek_ai I guess there is quite a fight for whoever is going to top the LLM charts by the end of 2025