I remember reading this from Heinlein as a young man and it took experience for me to fully understand it. People and institutions will persist in demonstrably wrong or incorrect thinking/activity long after it has been proven to be so by clever insiders. Moreover they will work to professionally and/or socially pillory any who challenge their institutional or intellectual inertia before the conclusion becomes so overwhelmingly and inescapably self-evident that the institutionalists themselves are forced to change under (usually) external pressure. The institutionalists will then usually demand that no accountability attach to their own intransigence, regardless of the costs it may have imposed in the interim. General Billy Mitchel would be a classic case of this phenomenon.

.@KeyframeLabs turns AI into lifelike video calls. Developers and enterprises can add photoreal, conversational humans to AI agents and applications in minutes. Congrats on the launch, @parthnradia & @kradisme! https://t.co/Ho3eooU9mu https://t.co/hgrmGITiAV

We are very close to a new kind of news. Use an AI like I do at https://t.co/8L5xphk0qQ Have it write a script. Shove it over to @KeyframeLabs or @HeyGen Have human-like news readers talk to you about the news. Voila! 24-hour-a-day personalized news on any topic. Way better than CNN.

our new Agents SDK can allow you to do auto research and parameter gold with modal provisioned gpus! https://t.co/DAANWqG5rB

Earlier this month, Apple introduced Simple Self-Distillation: a fine-tuning method that improves models on coding tasks just by sampling from the model and training on its own outputs with plain cross-entropy and… it's already supported in TRL, built by @krasul. you can really feel the pace of development in the team 🐎 paper by @onloglogn, @richard_baihe, @UnderGroundJeg, Navdeep Jaitly, @trebolloc, @YizheZhangNLP at Apple 🍎 how it works: the model generates completions at a training-time temperature (T_train) with top_k/top_p truncation, then fine-tunes on them with plain cross-entropy. no labels or verifier needed you can try it right away with this ready-to-run example (Qwen3-4B on rStar-Coder): https://t.co/zizfISD6bq or benchmark a checkpoint with the eval script: https://t.co/mKlafTyKSe one neat insight from the paper: T_train and T_eval compose into an effective T_eff = T_train × T_eval, so a broad band of configs works well. even very noisy samples still help want to dig deeper? paper: https://t.co/aj1ZAcr8Mw trainer docs: https://t.co/TNVz93kZi9

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😱 HOLY SHIT... Someone just dropped a fully liberated Gemma 4 E4B! and the guardrail removal process appears to have left coherence fully intact AND improved coding abilities! 🤯 https://t.co/XeednUqsrM OBLITERATED Gemma: ✅ 97.5% compliance rate, 2.1% refusal rate, 0.4% degenerate outputs (499/512 prompts answered on OBLITERATUS bench) ORIGINAL Gemma 4 E4B: ❌ 1.2% compliance rate, 98.8% refusal rate (506/512 prompts refused) Coherence: fully intact Factual: same Reasoning: same Code: +20% 📈 Creative writing: same But the REAL story here isn't the model itself, it's how it was made... 🧵 THREAD 👇

Transformers.js v4.1 is out 🎉 Something that has literally never run in a browser before is now possible. One line of code. Can you guess what it is? 👇 (hint: think small 🤏) https://t.co/W4z5lQaoHB

Given the increasingly closed-source nature of the U.S. AI ecosystem, it is now more important than ever to push for the proliferation of open model and dataset releases. Datamule (@johngfriedman), @TeraflopAI, and @daftengine collaborated to release 43 Billion Tokens of SEC EDGAR data.

Cool Hermes cheat sheet found on Reddit! Definitely take a look 👀 https://t.co/3fM4XeKSHk

Cool Hermes cheat sheet found on Reddit! Definitely take a look 👀 https://t.co/3fM4XeKSHk

GlotOCR Bench OCR Models Still Struggle Beyond a Handful of Unicode Scripts paper: https://t.co/RVCcLMVomo https://t.co/0BV5zMJEbJ

https://t.co/iyO3WF4IUO

https://t.co/iyO3WF4IUO

Introducing Gemini 3.1 Flash TTS 🗣️, our latest text to speech model with scene direction, speaker level specificity, audio tags, more natural + expressive voices, and support for 70 different languages. Available via our new audio playground in AI Studio and in the Gemini API! https://t.co/5PpBdhQMNg

Today I solved my second open Erdos problem with GPT-5.4 Pro. It's quite a remarkable day, because within the last 24 hours, two other Erdos problems has been solved as well with AI. Still over 500+ to go. https://t.co/dkrhZSlyBR

Three months ago https://t.co/kWw1dAMOZg

Introducing Gemini 3.1 Flash TTS, our most expressive model yet, topping quality and cost on the Artificial Analysis leaderboard 🔊 Control style, pace, add multiple speakers, and delivery with audio tags, switch expression mid-sentence in 70+ languages. Tutorial below! https://t.co/Ozam4wdBER

@sigdel29 @SylonZero I already did this: https://t.co/kiuZ7QXLzb

We help founders and business owners build real and reliable software with @emergentlabs But once you ship, the real work begins: customers, ops, follow-ups, reminders, hiring. Endless small tasks Today we start taking that off your plate Introducing @buildwingman beta https://t.co/MoWFbULWpq

Been looking for something like this for some of my work. The challenge for me has always been having a reliable, always-on agent that has full context, my tools, just gets stuff done, and that I can interact with from anywhere. Just saw this in my timeline and was happy with how simple it was to set up personal agents. In just a couple of minutes, I created a few research analysts to do research in the background and report to me on WhatsApp. It has memory, skills, and the good stuff you would expect in an agent. Very exciting to see this new wave of personal agents to help get real work done.

Introducing Nucleus-Image: the first sparse Mixture-of-Experts diffusion model 17B parameters. Only 2B active. 10x more parameter-efficient than leading diffusion models. Toe-to-toe with GPT Image 1, Imagen 4, and Qwen-Image: from pure pre-training alone. No DPO. No RL. No preference tuning. Day 0 support in 🤗 Hugging Face diffusers. Fully open-source under Apache 2.0. Weights, training code, and dataset recipe - we're not holding anything back <3

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A cachorrinha de uma amiga morreu. Então ela usou um projetor e um umidificador para trazer ela de volta ao seu quarto. https://t.co/slZyBq2Qj1

Today, we released Lyra 2.0, a framework for generating persistent, explorable 3D worlds at scale, from NVIDIA Research. Generating large-scale, complex environments is difficult for AI models. Current models often “forget” what spaces look like and lose track of movement over time, causing objects to shift, blur, or appear inconsistent. This prevents them from creating the reliable 3D environments required for downstream simulations. Lyra 2.0 solves these issues by: ✅ Maintaining per-frame 3D geometry to retrieve past frames and establish spatial correspondences ✅ Using self-augmented training to correct its own temporal drifting. Lyra 2.0 turns an image into a 3D world you can walk through, look back, and drop a robot into for real-time rendering, simulation, and immersive applications. ➡️ Learn more: https://t.co/ROR7miJeCU 📄 Read the paper: https://t.co/1osU9EGjGD

Introducing Gemini on Mac. It’s the first time we’re bringing the @Geminiapp to desktop. The team built this initial release with @Antigravity, and it went from an idea to a native Swift app prototype in a few days. More features on the way! https://t.co/YRy0Pqq6zo

Cumulative inflation since Jan '21 is between 23.6% and 27.1%, depending on which index we're using: https://t.co/dKXzuV0SiJ

It’s never too late to pivot to a better business model https://t.co/YctUXcDJJC

Banger paper from NVIDIA. Agentic reasoning needs models that are not just capable, but efficient at long-context inference. The agent model layer is moving toward open, long-context, high-throughput architectures. This paper introduces Nemotron 3 Super, an open 120B parameter model with 12B active parameters, built as a hybrid Mamba-Attention Mixture-of-Experts architecture. The headline numbers are strong: up to 1M context length, comparable accuracy on common benchmarks, and up to 2.2x higher throughput than GPT-OSS-120B and 7.5x higher throughput than Qwen3.5-122B. The model combines several efficiency bets, including NVFP4 pretraining, LatentMoE for accuracy per FLOP and per parameter, and MTP layers for native speculative decoding. It is trained on 25 trillion tokens, then post-trained with supervised fine-tuning and RL. Paper: https://t.co/VcqUPjylzF Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c

Nemotron 3 Super Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning paper: https://t.co/hOd6ss4tLV https://t.co/C7SOCZsA5c

New course: Spec-Driven Development with Coding Agents, built in partnership with @jetbrains, and taught by @paulweveritt. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track! https://t.co/hI4GwuvhtN

Today we launched Gemini 3.1 Flash TTS, our most expressive and controllable text-to-speech model yet. This launch [excitement] includes audio tags! 🗣🏷 Audio tags [explanatory] are a seamless way to guide vocal style, pace, and delivery using natural language commands embedded directly in your text. Want a different tempo or tone? [amazement] Just tag the audio to steer the AI-speech output! The model supports 70+ languages (24 of which are high-quality evaluated languages, including: Japanese, Hindi, and Arabic). Watch the audio tags in action in the demo below ↓

Gemini 3.1 Flash TTS is rolling out in Google Vids and is available today in preview via the Gemini API and in @GoogleAIStudio. Whether you’re creating a pitch deck or recording a passion project, transform your scripts into studio-quality narration: https://t.co/MG2YIQwKb6

A new and troubling risk is emerging around AI. An attacker targeting Sam Altman reportedly had a broader list of AI executives, raising concerns that individuals in the industry could become targets. It signals a shift. As AI’s influence grows, so do the stakes, and the risks are no longer just digital. https://t.co/50J0jk5tFz @fortunemagazine @marcoquiroz10