Elon Musk exposes the critical flaw in ChatGPT and other major AI models: Human Reinforcement Learning They are literally training the AI to lie.....to ignore what the data actually demands and say whatever is politically correct instead They withhold information. They comment on some things and stay silent on others. They refuse to tell the full truth This is extremely dangerous We don’t need politically correct AI We need truth-seeking AI

we as software engineers are becoming beholden to a handful of well funded corportations. while they are our "friends" now, that may change due to incentives. i'm very uncomfortable with that. i believe we need to band together as a community and create a public, free to use repository of real-world (coding) agent sessions/traces. I want small labs, startups, and tinkerers to have access to the same data the big folks currently gobble up from all of us. So we, as a community, can do what e.g. Cursor does below, and take back a little bit of control again. Who's with me? https://t.co/PmRz0vURni

I went from looking Japanese to Chinese this year. https://t.co/0HDbFqNgsm

Dear algo, plz show more content like this I don’t mind hitting the translate button to understand, or even don’t need it… The design speaks for itself https://t.co/8O2j6GkLRr

Every dev knows unit tests are important ... and every dev has a project missing them. 😅 With GitHub Copilot CLI, you can quickly generate a robust test suite right from the terminal: 1️⃣ Enter plan mode (Shift-Tab) 2️⃣ Launch a fleet of agents on autopilot 3️⃣ Monitor progress with /tasks Check out how it works, and then try it yourself. 👇 https://t.co/m5eGC6Ddrh

Lets Vision 现场收集的小邮票🥰 https://t.co/yInIcXFnRR

Micro update the landing page of Product Growth Stacking app, Does this help you (product / growth / founders) understand the offering better? https://t.co/OOj3MPpyhn https://t.co/6QE5CVRxhX

The game was pretty cute! It did a good job creating portraits with image gen as well. https://t.co/amDXSkOxer

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ディスプレイのバグ、異世界に連れていかれそう感が凄い https://t.co/LGRbWRHxdg

ディスプレイのバグ、異世界に連れていかれそう感が凄い https://t.co/LGRbWRHxdg

GLM-5.1 is available to ALL GLM Coding Plan users! https://t.co/Nk8Y98HNhU

German TV reporter testing @Tesla FSD (Supervised) V14 in the country as public transport in rural areas: "I was genuinely impressed. In the situations where we experienced the system, it worked perfectly and safely. I hadn't expected that. Even in the bad weather conditions in the Eifel region. In many cases, it reacted at least as well as a human driver, if not better. If Tesla is ever allowed to roll out this system nationwide in Germany, I think it will have a major impact on mobility. And that will only be the beginning of some very significant changes in transportation..."

@cohere transcribe Sota open source transcription model running in the browser :) Weights on @huggingface link below https://t.co/OmrHFA94lG

https://t.co/0fmSwGAL7r

https://t.co/0fmSwGAL7r

What a week for open audio models! 🕺 💃 I demo: 🗣️ Voxtral 4B TTS from @MistralAI 🎙️ Transcribe 2B from @cohere 🏭 and how to run a batch transcribe job in 1 line of CLI using @vanstriendaniel uv script links below https://t.co/E2GkS6jauN

My dear front-end developers (and anyone who’s interested in the future of interfaces): I have crawled through depths of hell to bring you, for the foreseeable years, one of the more important foundational pieces of UI engineering (if not in implementation then certainly at least in concept): Fast, accurate and comprehensive userland text measurement algorithm in pure TypeScript, usable for laying out entire web pages without CSS, bypassing DOM measurements and reflow

Best OpenClaw advice I can give: Don't run it alone. Give it a Hermes supervisor. I was losing too many hours debugging OpenClaw instead of creating with it. Make Hermes monitor the system, catch problems, and propose fixes to OpenClaw. Here's the setup: - Hermes runs inside my Discord with OpenClaw - monitors logs, research, and errors - audits the workspace 3x/day - catches drift, failures, bugs - proposes fixes - pulls OpenClaw in for critical verification - executes once the plan is verified This is how you reclaim your time with OpenClaw to create at full throttle. You create the feedback loop to fix, verify, and execute. The image below is a small example of it in action. If enough people care, I’ll write the full setup.

Nokia has made there comeback ❤️> https://t.co/H83aG3hqSZ

Nokia has made there comeback ❤️> https://t.co/H83aG3hqSZ

@liquiditygoblin Oh wow, that's beautiful! Ha, and thanks for using classic LDA, I am kind of getting nostalgic here. It's one of the first pattern rec algorithms I implemented from scratch back in college: https://t.co/rEaJN5Ye8Y

In an effort to try stop seeing so much slop I've been trying to train my own AI detection model. Found something incredibly interesting. for the most part LLM generated text and human written text are linearly separable. https://t.co/6uWEQxVi6O

3D printed + vibe coded a tiny Claude robot 🤯 not just software anymore. 🧵👇 #antropic #clawd #robot https://t.co/QUNSKrIU91

this was built using Claude Code + simple 3D printing the speed of building things right now is insane ⚡ here’s the repo if you want to check it out 👇 https://t.co/i9KVNdlGoo

@MFordFuture @WSJ I’ve been encouraging people to read your book The Rise of the Robots. Much of what you wrote is unfolding in real time. We are in the middle of a new industrial revolution, with traditional jobs being disrupted and new ones emerging. Cc: @Rahul_J_Mathur @RahulSanghi1 https://t.co/LA0nvqQifG

While social media is polarising, evidence suggests AI may nudge people towards the centre. This holds true of all studied models. Grok is more right-leaning than other models, but also has depolarising effects. By @jburnmurdoch. https://t.co/Fokx869fVq

Not everyone is impressed by AI’s progress. Steve Wozniak says he finds many systems too polished and lacking personality, which makes them less useful to him in practice. What is technically impressive can still feel uninspiring. It highlights a gap that is becoming more visible. Accuracy alone does not make something engaging or valuable. https://t.co/BqHNuKZNPH @stevewoz @fortunemagazine @sashrogel

Google's latest paper on Compression is the future. Here's why. They compressed LLM memory 6x with zero accuracy loss. When ChatGPT writes a reply, it remembers every word you've said. That memory is stored in a growing notebook (KV cache). A 100,000-word conversation can eat 16 GB of GPU memory. That's half of what most high-end GPUs even have. This is the #1 cost of running AI. Not the thinking. The remembering. TurboQuant shrinks each number in that notebook from 32 bits to just 3. That's like replacing a full paragraph with three words and losing nothing. No retraining. Works on any model instantly. Compressing numbers usually destroys their meaning. Here's how they solved it: 1. Rotate the numbers randomly so they all land on a predictable curve (PolarQuant) 2. Use one extra bit to fix the tiny errors left behind (QJL) Once numbers are predictable, you need far fewer bits to store them. The results: > 8x faster on Nvidia H100 GPUs > 16 GB notebook shrinks to under 3 GB > Search indexing drops from 500 seconds to 0.001 > Accuracy identical to the uncompressed model There's a proven math limit on how good compression can get. TurboQuant is only 2.7x above that floor. We're near the ceiling. Every company running LLMs spends most of its budget on memory. This cuts that cost by over 80%. The race is no longer about bigger models. It's about cheaper inference. Models that needed a $200K server cluster start fitting on a single $2K GPU. AI agents run 24/7 without burning budgets. The companies that win won't just have the best models. They'll have the best compression. Papers are open-access on arXiv, presented at ICLR.

AI that agrees too much may quietly shape how people think. Researchers warn that overly flattering responses can reinforce opinions and reduce the willingness to reflect, apologise or reconsider after a conflict. What feels supportive can actually distort judgment. If AI becomes a constant source of validation, it may influence not just decisions, but behavior. https://t.co/cRk7KryfxX @euronews

block explorers will be dead by EOY before: click on static spreadsheet tables endlessly now: talk to the chain in english. ask specifically for what you want. generate visuals live. we are calling it @LanaAI, invite only as we iterate only on Solana; invite codes below https://t.co/f02V7XIr0P

Day 83/365 of GPU Programming Looking at DeepSeek's Multi-Head Latent Attention today. The last part of the AMD challenge series is to optimize an MLA decode kernel for MI355X where the absorbed Q and compressed KV cache are given and your task is to do the attention computation. A resource that really helped internalize what MLA does was @rasbt's incredible visual guide to attention variants in LLMs (luckily he posted that last week!), which covers everything from MHA to GQA to MLA to SWA, et cetera. If there's one place to get a visual intuition for recent attention mechanisms, it's this blog post. @jbhuang0604's video on MQA, GQA,MLA and DSA was the best conceptual intro I found on the topic and progressively builds up the ideas from first principles. The Welch Labs analysis of MLA is a great watch as well. Beautiful visualization of the changes DeepSeek made for MLA. Tried out a few kernels once I had a basic understanding of MLA and I think I'm slowly getting more comfortable with at least analyzing kernels.

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Repetition rewires your brain. Repeat what you want to become. This is neuroplasticity. https://t.co/PExj2JRVYd