u/stealthispost

> ...messy/unverifiable for RL. >   >   > — Luis Garicano

Source: https://x.com/lugaricano/status/2107853818784272623

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> What if the jagged frontier is mainly math + code (which you can push arbitrarily far with RLVR), and everything else starts to plateau because it is still bottlenecked by human generated data? >   > Model performance in non-verifiable areas has kept improving steadily, albeit much >   > — François Chollet

Source: https://x.com/fchollet/status/2107625225768858076

> First, built a physics-based procedural generator for the cylinders, complete with zoning, ag constraints, etc. (thanks Opus 5.5 >   > @AnthropicAI >   > ) >   >   > Then build a rendered cinematic fly-through: >   >   > Then take screen caps and turn them into real-looking images: >   >   > Then feed all that into a video model. Need to improve the camera work here a bit but not bad. >   >   > — Max Williamson

Source: https://x.com/MaxXWilliamson/status/2106156596250411101

> ...professional work completely, bout about 90% they say. >   > What a fantastic time to be alive. I love it. >   >   > https:// >   > github.com/storytold/phot >   > ocraft >   > … (Photoshop) >   > https:// >   > github.com/storytold/vect >   > orcraft >   > … (Illustrator) >   > https:// >   > github.com/storytold/film >   > craft >   > … (Premiere) >   > https:// >   > github.com/storytold/ligh >   > tcraft >   > … (Lightroom) >   > https:// >   > github.com/storytold/prin >   > tcraft >   > … (Acrobat Pro) >   > https:// >   > github.com/storytold/effe >   > ctcraft >   > … (After Effects) >   > https:// >   > github.com/storytold/desi >   > gncraft >   > … (InDesign) >   >   > — Chubby

Source: https://x.com/kimmonismus/status/2107618163726921853

Humanity is about to spend more on AI than the total spend of every military on Earth combined.

According to Gartner, global spending on artificial intelligence is projected to reach $2.67 trillion in 2026.

For comparison, the entire world's military expenditure was $2.89 trillion in 2025.

And by 2027, AI spending is forecast to reach $3.64 trillion.

We are witnessing the largest technological mobilisation in human history.

And perhaps the most inspiring part is that intelligence isn't just another technology. It's a tool for developing every other technology.

For the first time, building intelligence is attracting investment on the scale of the entire global military establishment.

Sources:

• Gartner — Worldwide AI spending forecast, September 2026
• SIPRI — Global military expenditure, April 2026

> buy tickets >   >   > — CIX

Source: https://x.com/cixliv/status/2107917004200194307

> On 400 claim-verification questions built from SEC filings, Jev tied GPT-6 Astra at 97.5% accuracy for ~$0.025 per 1,000 cases versus $12.29. >   > It is also quite fast: at 32 judgments about one document, Jev’s median latency was 194x lower than Astra’s. >   >   > We did not see these gains carry over to legal reasoning. Jev ranked last on our 12-task LegalBench slice. When we tuned its confidence threshold for a 1% error budget, Jev automated 95% of held-out claim-verification cases—but at 1.6% error. >   >   > Despite speed and cost being high priority, task fit should come first. Strong results on one benchmark don’t guarantee reliable performance on your workload or that confidence threshold will hold up on unseen cases. >   > For the full report, visit >   >   > — Vals AI

Source: https://x.com/ValsAI/status/2107559370208997711

— @tucefanacc

Source: https://www.tiktok.com/@tucefanacc

> Core runs local models 24/7 entirely on-device. No data ever leaves your home. >   > Each core comes with: > - NVIDIA RTX PRO 4000 Blackwell SFF GPU > - 64GB DDR5 RAM > - 1TB NVMe M.2 SSD > - AMD Ryzen 5 7600 CPU >   >   > You connect and talk to Core through your laptop, mobile app, or iMessage. >   >   > Core has full context from all of your apps, files, and devices. >   > It recognizes trends in sleep data from my Whoop and connects them to late meetings on my calendar, finding actionable ways to improve my sleep. >   >   > Core is a full computer - it has its own file system, browser, and tools to take action just as you would. >   > It browses the web for the best moving companies in San Francisco, pulls their phone numbers, and calls them to find who's available for the lowest price. >   >   > You can use your Core to power inference in other apps and custom harnesses like OpenCode, OpenClaw, and Hermes. >   > Core reaches over 87 tok/s on Qwen-3.8-27B and 32 tok/s on Qwen-3.8-Next. >   >   > — Zain Javaid

Source: https://x.com/zainmfj/status/2107146556176798081

> Thanks to Bloomberg ( >   > @business >   > ) that covered what we’re launching today and the regulatory path that got us here. >   >   > (3/9) Until now, AI in healthcare has been limited to answering questions, summarising information, or assisting a clinician. We’ve seen a handful of organizations approved to refill prescriptions initially written by a human doctor with AI. >   > Nolla is taking a big step further >   >   > (4/9) This is a huge breakthrough because healthcare has always had a fundamental constraint: >   > People. >   > There are only so many doctors and only so many hours in the day. For millions of Americans, that means they just never get the care they need. >   >   > (5/9) Every day, doctors across the U.S. spend hours treating routine, well-understood cases. That leaves less time for the complex, high-risk cases where their expertise is needed most. >   > AI can make an enormous difference by taking on more of that routine care. >   >   > (6/9) We’re starting with acne care in Utah. >   > ~100,000 Utahns have active acne. >   > 90% of them have never seen a dermatologist. >   > In a state with the highest melanoma incidence in the country, dermatologists are already stretched thin by routine cases AND still don’t have enough >   >   > — Luis Wenus

Source: https://x.com/luiswenus/status/2107123349348298800

[deleted]

> Powered by >   > @Scenario_gg >   > We help creative teams orchestrate and scale everything they produce. >   > Try it out today >   >   > — Emm | scenario.com

Source: https://x.com/emmanuel_2m/status/2107142680782438466

> ...nothing bad happens, and kind of figure out how to dole out the benefits.’” >   > He calls that “a completely unacceptable tradeoff.” >   >   > — Chris

Source: https://x.com/ChrisGPT/status/2106873284113424574

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> https://t.co/fJsAXXm4gO >   > — Brendan Bordelon

Source: https://x.com/BrendanBordelon/status/2106852703842492511

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> Replying to @brendanbordelon

> Our fits imply recent growth of about 1.8× per month at the median and 2.2× at the 90th percentile, or doubling times of 34 and 27 days, respectively. >   > As of mid-August, daily usage was valued at around $600 for median researchers, and over $7,000 for the 90th percentile. >   >   > These figures value usage at API prices, so they don’t measure OpenAI’s actual costs. Early growth partly reflects more researchers adopting agents, in addition to existing researchers using agents more intensely. >   > See more on our site: >   >   > — Epoch AI

Source: https://x.com/EpochAIResearch/status/2107174397698289762

— Pessimists Archive

Source: https://x.com/PessimistsArc/status/2106808432330215744

> ...𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐜𝐚𝐥 𝐜𝐡𝐚𝐧𝐠𝐞 𝐭𝐡𝐚𝐭 𝐡𝐚𝐝 𝐭𝐚𝐤𝐞𝐧 𝐩𝐥𝐚𝐜𝐞 𝐨𝐧 𝐞𝐚𝐫𝐭𝐡. 𝐔𝐬𝐮𝐚𝐥𝐥𝐲 𝐭𝐡𝐞 𝐫𝐞𝐬𝐢𝐬𝐭𝐚𝐧𝐜𝐞 𝐜𝐚𝐦𝐞 𝐟𝐫𝐨𝐦 𝐭𝐡𝐨𝐬𝐞 𝐠𝐫𝐨𝐮𝐩𝐬 𝐰𝐡𝐨 𝐬𝐭𝐨𝐨𝐝 𝐭𝐨 𝐥𝐨𝐬𝐞 𝐢𝐧𝐟𝐥𝐮𝐞𝐧𝐜𝐞, 𝐬𝐭𝐚𝐭𝐮𝐬, 𝐦𝐨𝐧𝐞𝐲...𝐚𝐬 𝐚 𝐫𝐞𝐬𝐮𝐥𝐭 𝐨𝐟 𝐭𝐡𝐞 𝐜𝐡𝐚𝐧𝐠𝐞. 𝐀𝐥𝐭𝐡𝐨𝐮𝐠𝐡 𝐭𝐡𝐞𝐲 𝐧𝐞𝐯𝐞𝐫 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐭𝐡𝐢𝐬 𝐚𝐬 𝐭𝐡𝐞𝐢𝐫 𝐫𝐞𝐚𝐬𝐨𝐧 𝐟𝐨𝐫 𝐫𝐞𝐬𝐢𝐬𝐭𝐢𝐧𝐠 𝐢𝐭. 𝐈𝐭 𝐰𝐚𝐬 𝐚𝐥𝐰𝐚𝐲𝐬 𝐭𝐡𝐞 𝐠𝐨𝐨𝐝 𝐨𝐟 𝐡𝐮𝐦𝐚𝐧𝐢𝐭𝐲 𝐭𝐡𝐚𝐭 𝐫𝐞𝐬𝐭𝐞𝐝 𝐮𝐩𝐨𝐧 𝐭𝐡𝐞𝐢𝐫 𝐡𝐞𝐚𝐫𝐭𝐬.” >   >   > — Pessimists Archive

Source: https://x.com/PessimistsArc/status/2107081120269439366

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> People resist new technology, new ideas. As Dostoevsky opens Crime & Punishment with: "Taking a new step, uttering a new word, is what people fear most." I think fear points the way toward progress. Here's an ad from 1915 (via @PessimistsArc) suggesting a horse outperforms a car. >   > — Lex Fridman

Source: https://x.com/lexfridman/status/1203302464960827392

> ...Its predicted ability to sort electrons by spin appears to have been hiding in plain sight for 27 years. >   >   > As far as we know, YBaMnFeO₅ is a new design that was never made or proposed as this kind of magnet. Simulations predict a 2.35 eV band gap and magnetism above room temperature, but the Mn/Fe checkerboard it needs may scramble during synthesis. >   > KV[Cr(CN)₆] stays magnetic up to >   >   > These are predictions for perfect crystals; nobody has measured the spin sorting yet. The next step is to make KV[Cr(CN)₆] again and measure it. >   > It is clear that models will begin to make huge strides in material science quite soon. >   > The inputs, raw outputs and scripts behind >   >   > — Vals AI

Source: https://x.com/ValsAI/status/2107204457738256749

> meanwhile >   > — Fito Prolog >   >   > 20 years in jail for me >   > — CIX

Source: https://x.com/cixliv/status/2105055041451336100

> ...insights into materials in extreme conditions. The resulting material is extremely lightweight yet strong, far better performing than existing structures. By organizing "simple" carbon atoms into hierarchical graphene architectures, unique materials can be designed that redistribute forces, accommodate deformation, and confine damage, the AI identified design principles for resisting catastrophic failure. The AI built the atomistic simulation instrument itself "from scratch"; and then conducted experiments autonomously that revealed when alignment strengthens a material, when hierarchy protects it, and when an apparently promising design fails. This is a frontier in designing matter at its ultimate thinness - controlling how mechanical failure unfolds through the organization of individual atoms. >   > Here is what we did: >   > We asked an AI to build and use a scientific instrument. It wrote the force engine, structure generators, loading procedures, analysis tools, and experiment database. The independent campaign ran for multiple days without scientific intervention. > We required the instrument to pass physical and numerical tests. The implementation passed twenty validation tests and reproduced reference energies to approximately 10⁻¹³ eV per atom in the tested configurations. Every proposed design then faced the same reactive interatomic model. > We required predictions before results, creating a loop of world model building and falsification/verification. The AI had to commit to what unseen designs would do, then run the simulations. Incorrect predictions became opportunities to identify missing mechanisms. >   > What emerged is a set of physical design principles: >   > The atom-scale arrangement of matter controls strength. The AI first showed how and why strength varied by more than sixfold across architectures. Similar amounts of carbon produced very different resistance to failure because they organized the load-bearing connections differently. > Rotating a pattern can change the mechanism of failure. Angled slit arrays revealed three regimes: neighboring slit tips link, intervening ligaments rotate, or short bridges bend. A rule based only on the remaining cross-section misses these changes in connectivity and motion. > Hierarchical structuring works under identifiable conditions. At the original scale, much of its apparent strength advantage can be explained by alignment. With greater separation between structural levels, selected hierarchical designs became about 25% stronger than same-mass single-level controls and showed larger integrated stress-strain responses. Veins redistribute load, compartments localize damage, and the architecture changes how cracks propagate. > A failed prediction is crucial to reveal the next experiment. Some proposed rules survived targeted tests; others failed. Longer loading preserved the broad architectural strength contrasts while revealing additional deformation and, in some cases, later stress peaks. The scientific value lies in identifying both the rule and its boundary as the AI punctures known scientific knowledge. > The instrument opens an extremely complex design space. The AI was able to expand the design languages into an open atlas of hundreds of thousands of atomically explicit structures. >   > The deeper implication is that AI can construct an executable connection between equations, experiments, and explanations. Physical reasoning is something they can implement, interrogate, and revise. >   > A scientific instrument extends what a scientist can observe; and an AI that builds such an instrument extends the experiments it can perform, and the questions it can ask, starting from basic principles of how atoms interact based on quantum mechanical ground truth. >   > Models building models, with physical evidence shaping recursive reasoning loops. >   > — Markus J. Buehler >   >   > 2026 feels like next stage of AI development in various domains, including the scientific method. >   > Great research. >   > — Willi Schroll >   >   > Thank you totally agree >   > — Markus J. Buehler

Source: https://x.com/ProfBuehlerMIT/status/2104857542350295433

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> AI that reasons from first principles, atom by atom. Physics becomes native to the reasoning process itself, and drives a powerful self-evolving loop where intelligence and matter merge. https://t.co/YIfGuR3xyG >   > — Markus J. Buehler

Source: https://x.com/ProfBuehlerMIT/status/2104591839617511812

> Real-world locomotion does not follow a single easy-to-hard axis. Terrain type and geometry compound with linear and angular velocity specifications. >   > In our scaled setup, their Cartesian product forms 600 tasks, making a manually ordered curriculum impossible. >   >   > LP-ACRL removes the need to predefine task difficulty. It measures task-wise learning progress from episodic-reward changes, then uses a softmax to shift sampling toward the current learning frontier, where tasks show stronger positive competence gains at that stage. >   >   > The result is a moving curriculum: when progress on a task slows, training shifts toward tasks with stronger learning signals, while revisiting previously learned skills. >   >   > We evaluate LP-ACRL on multi-level velocity tracking, multi-type rough-terrain traversal, and a scaled 600-task space. It outperforms handcrafted and automatic curriculum baselines in convergence speed and final competence. >   >   > After teacher–student distillation, a single policy transfers to ANYmal D and achieves: >   > • 3.0 m/s on flat terrain > • 2.5 m/s on challenging terrain > • Up to 3.0 rad/s angular velocity across all tested terrains >   >   > — Robotic Systems Lab

Source: https://x.com/leggedrobotics/status/2104971771531190310

> we are Yes Developers now >   > Anyone know how to disable those constant Yes No prompts in VSC’s Claude extension??? >   >   > — Peter Mick

Source: https://x.com/ThePeterMick/status/2103875676352676037