Massively Parallel Procrastinator

Been silent for 2 days, Guess whattt **Drum rolls** Finally after all these months of searching, got a freaking card... aka value king 3090 !! Used one, but who cares. Now I can run lot more on my machine 24GB > 12GB Qwen3.6 27b here i come !!!

Announcing a new division of Midjourney called "Midjourney Medical"

100+ hands-on courses. Taught by practitioners who've deployed in live environments. The gap between knowing and doing is where breaches happen.

Making a change to the Hermes Agent Curator that will make it so it only Prunes unused skills by default, it will no longer consolidate skills unless you opt in in the config or dashboard. It was costing upwards of 16$ per week to operate for some using frontier models and causing some confusion for other users. This is a better middle ground for a default scenario.

JUST IN: Meta’s CTO says morale is near “the worst it’s ever been” — leadership will offer increased snack budgets to lift spirits.

Today, we enable AutoResearch in the physical world for the first time! Introducing ENPIRE: we give 8 Codex agents a fleet of robots, an allocation of GPUs, and generous token budget. We set them free with a simple goal: solve the task as quickly as possible, keep the robots busy but stay safe, don't waste precious compute. Make no mistake. Then humans step aside and our watch begins. The robot fleet starts to come alive: they learn to look for visual clues, reset the scene, practice novel skills, tinker with control stack, read papers online, debate, reflect, get stuck, and try again directly on the hardware. All we did is to give Codex an API to the world of atoms, and the rest is emergence. ENPIRE is able to solve high-precision tasks like tying zip-ties, organizing fine pins, and installing GPUs all by itself. We also discovered a new type of "physical scaling": 8 robots exploring in parallel improves significantly faster than fewer ones. A part of our NVIDIA GEAR lab now self-improves tirelessly over night. We just read the reports in the morning. /goal: we all take a holiday and Jensen wouldn't even notice ;) We will be open-sourcing everything, so you can host your self-running robot lab at home too! Deep dive in the thread:

Another workstation converted. Bye Bye OpenClaw 👋 Welcome Hermes 🚀 No regrets.

No one wants to admit this, but the Steve Wozniak / Steve Jobs era of "technical founder + business founder" is over. For 40 years the model was the same. One founder builds. One founder sells. That split made sense when writing code took a CS degree and distribution took a budget. It doesn't anymore. AI killed the distance between technical and non-technical. Now only one role really matters: The fullstack founder. The person who can do all of it, with AI carrying the weight. Here's what that actually means in 2026: 1. You can build, even if you've never written code. Agentic coding tools turn plain English into real, shippable products. You describe what you want, it writes it, you keep iterating. "I'm not technical" stopped being a reason to go find a cofounder. 2. You can fill your pipeline without a sales team. AI GTM agents like GojiberryAI watch for buying signals, run the outreach, and book demos while you sleep. No scraping lists, no firing off 200 cold DMs by hand. You just show up to the calls that are already warm. 3. You can create demand, not just chase it. One good post on X, LinkedIn or TikTok can put your product in front of millions, for free. Distribution used to be a budget line you needed money to unlock. Now it's a skill you learn and run yourself. 4. You can actually sell. None of the above matters if you can't get a real person to say yes. Selling is still the one thing no tool does for you, and the founder who can build, market AND close has an edge nobody can compete with. We started as 3 founders. With AI covering the work that used to need whole departments, we hit $3M ARR and 2,000+ customers in under 1 year. Each of us runs across product, outbound, content and sales. The founders winning today aren't the most technical. They're not the best marketers either. They're the ones who refused to pick a lane. You don't need a cofounder who completes you anymore. You need to become the whole stack.

This is pretty insane! You can run one of the top LLMs, GLM-5.2, at home on 3 x DGX-Sparks available at Amazon Best Buy.

We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a solid 1M-token context. GLM-5.2's new capabilities include: Solid 1M Context: A solid 1M-token context that stably sustains long-horizon work Advanced Coding with Flexible Effort: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency Improved Architecture: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20% Pure Open: An MIT open-source license — no regional limits, technical access without borders Supporting long-horizon tasks starts with making long context engineering-usable: the model must maintain quality across long, messy coding-agent trajectories, not just accept more tokens. A 1M context is easy to claim, but much harder to keep reliable under real engineering pressure. To this end, we substantially expanded 1M-context training for coding-agent scenarios, covering large-scale implementation, automated research, performance optimization, and complex debugging. The result is a long-context system that is not only wide in scope, but solid in execution: a practical substrate for sustained engineering work. This capability is reflected in GLM-5.2's performance on three long-horizon coding benchmarks. FrontierSWE measures whether an agent can complete open-ended technical projects at the scale of hours to tens of hours, spanning systems optimization, large-scale code construction, and applied ML research. On this benchmark, GLM-5.2 trails Opus 4.8 by only 1%, while edging out GPT-5.5 by 1% and Opus 4.7 by 11%. On PostTrainBench, where each agent is given an H100 GPU and evaluated by how much it can improve small models through post-training, GLM-5.2 outperforms both Opus 4.7 and GPT-5.5, ranking second only to Opus 4.8. On SWE-Marathon, an ultra-long-horizon software engineering benchmark covering tasks such as building compilers, optimizing kernels, and developing production-grade services, GLM-5.2 still has room to grow, trailing Opus 4.8 by 13% while remaining second only to the Opus series. Across all three benchmarks, GLM-5.2 is the highest-ranked open-source model, showing that its 1M context has translated into practical long-horizon delivery capability.