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https://t.co/idnz88Stdw
This really is an all time photo. A protestor shouting about the pros of immigration is interrupted by an Islamic terrorist throwing a bomb jumping over him. https://t.co/3TyBfewCYT
Democracy without secure elections is merely a facade https://t.co/KTLSYdsyXc
New this AM: Anthropic has filed its lawsuits against the Trump administration over the supply chain risk designation https://t.co/hmEB80FkYm
BREAKING: Alibaba tested 18 AI coding agents on 100 real codebases, spanning 233 days each. they failed spectacularly. turns out passing tests once is easy. maintaining code for 8 months without breaking everything is where AI completely collapses. SWE-CI is the first benchmark that measures long-term code maintenance instead of one-shot bug fixes. each task tracks 71 consecutive commits of real evolution. 75% of models break previously working code during maintenance. only Claude Opus 4.5 and 4.6 stay above 50% zero-regression rate. every other model accumulates technical debt that compounds with every single iteration. here's the brutal part: - HumanEval and SWE-bench measure "does it work right now" - SWE-CI measures "does it still work after 8 months of changes" agents optimized for snapshot testing write brittle code that passes tests today but becomes completely unmaintainable tomorrow. they built EvoScore to weight later iterations heavier than early ones. agents that sacrifice code quality for quick wins get punished when the consequences compound. the AI coding narrative just got more honest. most models can write code. almost none can maintain it.
Grok 4.1 is currently reviewing the entire corpus of EU legislation, one regulation at a time. 21 / 149,183 so far. Each with a single verdict: keep or delete. https://t.co/kkICWmoVSL
If you’re working with lots of slide decks and need a better way to search through them, Surreal Slides makes it simple 🌀 Built around LlamaParse, it parses presentation files into clean, structured data, turning raw slides into something AI can truly understand. Each slide is extracted, summarized, and organized before being stored in @SurrealDB for flexible retrieval. From there, you can query your entire presentation library in natural language through an agentic interface: no need to manually scan files or remember where a specific slide lives. Take a look at the demo below👇 GitHub Repository: https://t.co/jsTnjkUoED
You can now use Claude Code and GitHub CLI directly inside Perplexity Computer. We gave it an open issue on Openclaw. Computer: → Forked the repo → Wrote a plan to fix the bug → Opened Claude Code and implemented it → Submitted a PR via GitHub CLI https://t.co/MpVPchNqJa
Meta’s AI smart glasses are now facing a class-action lawsuit over privacy concerns. An investigation found subcontractor workers reviewing highly sensitive user footage captured by the devices. Wearable AI may be powerful, but it’s also forcing a new debate about surveillance, consent and who really sees what we record. https://t.co/9fsPtUZx9b @techcrunch @SarahPerezTC
BREAKING: Cluely CEO officially responds to TechCrunch https://t.co/EtAurp5zgZ
BREAKING: Cluely CEO officially responds to TechCrunch https://t.co/EtAurp5zgZ
With the passing of Khamenei, every leader that Peter invited to his Petoria pool party in “E Peterbus Unum” (2000) is now deceased, while Family Guy is still on the air. https://t.co/dJGbFRPhl4
Built an Apple Silicon / MLX port of your autoresearch — runs natively on Mac, no PyTorch needed. The loop found that depth=4 beats depth=8 on M4 Max because more optimizer steps > more parameters in a 5-min budget. https://t.co/BRvG6kLzuc @karpathy
Introducing, Runway Characters. Real-time intelligent avatars that turn the internet into a conversation. Deployable anywhere via the Runway API, Runway Characters can be customized in any way across every style. All with the ability to embed bespoke knowledge banks, custom voices and instructions. Start integrating Runway Characters directly into your apps, websites, products and services today. Available now at the link below.
The good/bad part about agentic codeing is the barrier to getting nerdsniped is now much lower https://t.co/CiGerRgM8H https://t.co/z6p0W229YM
Though bash is a completely valid REPL, the amount of time coding agents lose during experimentation because they iterate on scripts instead of a Jupyter-like in-memory REPL is basically dumb. Fixing 1 local bug should not require restarting the whole job. Need better scaffolds.
Penguin-VL Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders app: https://t.co/VZ8IvEdjN3 paper: https://t.co/XSM2GGVcCz https://t.co/ovxWSRJG0n
C++ devs: your AI-assisted flows just got even smarter! With the new symbol‑level context and CMake‑aware build tools, your agents now have access to rich C++ specific intelligence directly in VS Code. Learn more: https://t.co/ErgApqTzZc
shout-out to @nicopreme and @jxnlco for being based gods and hooking me up with a ChatGPT Pro subscription for my OSS contributions! cheers 🙇♂️ https://t.co/HACBr3Dvme
KARL Knowledge Agents via Reinforcement Learning paper: https://t.co/sTeBtxk5Ls
MatAnyone 2 is out on Hugging Face Scaling Video Matting via a Learned Quality Evaluator paper: https://t.co/KPMaG8teJ2 app: https://t.co/wkMpaOdoCh https://t.co/ZSQNrOKcv4
A major legal line has been drawn in the AI creativity debate. A U.S. court ruling reinforced that copyright law protects works created by humans, not machines. For “AI artists,” the message is clear, without meaningful human authorship, there may be no copyright. https://t.co/g1Nferwkod @futurism
A Threads user named Laushi Liu posted dashcam footage from his Tesla Model 3 on Sunday, March 8, showing the vehicle on “Full Self-Driving” mode at 23 mph near West Covina, California. In the video, the car approaches a railroad crossing where barriers have just come down — and drives straight through them. The timing is almost poetic: this video drops the day Tesla is supposed to finally hand NHTSA the data from its FSD violation investigation, after two deadline extensions. We’ll be watching to see whether Tesla actually delivers, and what that data reveals about just how common these railroad crossing failures really are.
Tesla 'Full Self-Driving' drives through railroad crossing barriers in viral video https://t.co/8GbihV7utb by @fredlambert
They haven't even discovered the sacred texts yet. https://t.co/aQE41MqaWn
The size of the pants I see on college campuses surpass anything we’ve ever attempted
They haven't even discovered the sacred texts yet. https://t.co/aQE41MqaWn
Learn how to run Qwen3.5 locally using Claude Code. Our guide shows you how to run Qwen3.5 on your server for local agentic coding. We then build a Qwen 3.5 agent that autonomously fine-tunes models using Unsloth. Works on 24GB RAM or less. Guide: https://t.co/JDPtuIJAZC
Announcing Copilot Cowork, a new way to complete tasks and get work done in M365. When you hand off a task to Cowork, it turns your request into a plan and executes it across your apps and files, grounded in your work data and operating within M365’s security and governance boundaries.
📸 New on Lovart: Multi-Angles Drag to rotate, tilt, and scale. One image, every angle, no prompt needed. → Subject Mode: move the subject directly → Camera Mode: move the virtual camera Like + reply + follow – 30 lucky winners get 300 credits each! https://t.co/n6VDphWw5Q
Is the rise of coding agents surprising or consistent with our predictions? Thanks for the question, @_NathanCalvin. https://t.co/fLdWDgSRAL The answer is: Both surprising and consistent. AI as Normal Technology (AINT) doesn't give us a way to predict the timing of specific capability advances, and we haven't tried to do that. But when it comes to understanding why coding agents work so well and what their impacts are likely to be, AINT is extremely helpful (and its predictions are consistent with what we observe so far). 1. Products, not just models. One key prediction is that model capability advances are generally not useful by themselves; building products is still necessary in order to meet people where they are, instead of forcing people to contort their workflows to fit the affordances of raw LLMs. That's exactly what we see with Claude Code and other agents. If we try to understand the success of coding agents as the result of model capability leaps, it doesn't make sense. Rather, coding agents have dozens if not hundreds of features, both big (like memory) and small (like rewinding or interruptability) that allow software engineers to integrate them into workflows. 2. Early adoption. Despite everything we hear on X, we're still in the early adoption phase. The median programmer (keep in mind that they work in a regulated industry like finance or healthcare) has barely heard of coding agents and is not yet using them in any serious way. 3. The speed of diffusion. As I've written before, the software industry has uniquely low diffusion barriers and programmers have a long history of embracing productivity improvements to continually migrate up the abstraction chain (machine code -> assembly -> compiled languages -> high-level languages -> frameworks -> AI-assisted programming). Because of this, software has "has never had time or the cultural inclination to ossify institutional processes around particular ways of doing things." I highly doubt that we are going to see the same speed of diffusion in other sectors. For example, see our analysis of AI in legal services here https://t.co/0kYIaT2UJJ 4. Labor market impacts. AINT predicted that in most cognitive jobs the result of AI adoption won't be replacing humans but shifting the role of humans to supervising AI systems. Of course we were hardly alone in making that prediction but it's good to see that this is what is happening in software. There's also the fact that in most white-collar jobs, if it gets cheaper to produce a unit of work, we will simply produce more of it — orders of magnitude more in the case of software (related to "Jevons paradox"). This is another factor that mitigates job loss risks.
Generally seems reasonable and appreciate your contributions here. One question - has the speed and purported efficacy of AI coding agent adoption surprised you? Or does it feel consistent with predictions you would have made from the AI as a normal technology worldview?
People are lying to you. These agents don't work as they promised. https://t.co/3Oyoi7i4zh
Microsoft seems to be launching its own branded version of Cowork (though I hesitate to discuss products I haven’t tried) A big question is whether it will continue to use lower-end models without telling you. Also whether it will keep up as the space evolves, or is it a one-off https://t.co/9ZkHEfZ6zr
