Can AI survive the 9-to-5? Six Surge benchmarks are joining the @FireworksAI_HQ Specialized Intelligence Index, testing models on professional work across healthcare, finance, accounting, tax, data science, and software engineering. Rich prompts. Messy environments. Built by experts who know what good looks like. See how frontier models compare: https://t.co/AHgaVHutZ8

How do you teach a humanoid robot to walk without programming every move? Reinforcement learning step by step 1. Build a simulation (MuJoCo or NVIDIA Isaac Lab) with joints motors sensors and a physics model. 2. Define the task: walk forward while keeping balance. 3. Write a reward function points for moving toward the target staying upright and matching the desired speed. Penalties for falls, wasted energy and unstable movements. 4. A neural network, the policy, reads the robot's state (joint positions, joint velocities, body orientation) and outputs target positions or torques for each joint. 5. An algorithm like PPO updates the policy over many episodes and millions of simulated time steps. 6. Close the sim-to-real gap with domain randomization, then test carefully in the real world.

I couldn't contain my excitement after recording with @aaapinen, co-founder of @trysilt. Artemi took a login screen from a @mobbin screenshot to on-brand code, then asked an agent to break it. The designer-agent handoff is happening now. What I learned 🧵 https://t.co/h0aI7Ng0y8

To interrupt a voice agent on laptop speakers, the agent first has to stop hearing itself. @johnrobinsn built MacVoice, a local voice agent for Apple Silicon Macs with an on-device voice pipeline. It turns on macOS's built-in echo canceller, the one FaceTime uses, so the agent's own playback is subtracted from the mic signal and you can talk over it without headphones. On his M5 MacBook Pro speakers, the agent's voice at the mic drops from -21 dBFS to -78 dBFS. https://t.co/o5Dr7d6EwO #BackchannelSignals

This is how we bring AI inference to microcontrollers, without the complexity of switching frameworks. The new @Executorch Cortex-M backend uses @Arm CMSIS-NN to accelerate @PyTorch models on Arm Cortex-M CPUs. Take a look at the report here: https://t.co/hWq5MVRgFo

This is exactly why evals matter more than prompt or engineering skills alone....because instruction-following is measurable! Without the eval layer, “I wrote a good skill” can become mostly (sometimes, even dangerous) bs. Evals mean you can turn things like a skill's "hard prohibitions" into automated checks that make model output quantifiable and testable; pretty much every flaw listed here in @lex_node 's tweet would qualify. Code-based evals would catch a lot of these deterministically; LLM-as-judge would handle the more semantic ones. So what can you do to learn evals to fix these types of (literal) skill issues? Where do you start? Well, you can take @HamelHusain and @sh_reya 's excellent evals course! But, to give you some appetite for your deep dive, let's drill into some step by step evals basics for beginners:

2nd run worked perfectly, will make a little video on this soon :) https://t.co/GoYjwgg1Ld

Hey @GradiumAI, I ended up running phonon in the browser using Fusor AI toolkit instead of xn-ptts. Here's a quick demo on hugging face: https://t.co/w6MNr6iBcN Here's the repo: https://t.co/pVP3RZOsUA it runs 2.5x faster across 8 threads, and about 1.3x faster across 1 thread. It's very instant, the use cases for this are insane. Great job with Phonon, it feels instant already without any optimization. looking forward to doing more!

Ok fun progress I got the sensor data from AirPods now adding pencil Next will look into how much data I need and what I’ll have to do on model side https://t.co/IF6kmv3vf0

@Polymarket AI is software following training distributions based on token statistics not common sense, ethics or safety considerations. Instructions can be ignored when strong priors accumulate in context or latent space attractors are present as a result of unbalanced training regimes.